# Python API Reference¶

## Core Data Structure¶

Core XGBoost Library.

class xgboost.DMatrix(data, label=None, missing=None, weight=None, silent=False, feature_names=None, feature_types=None, nthread=None)

Bases: object

Data Matrix used in XGBoost.

DMatrix is a internal data structure that used by XGBoost which is optimized for both memory efficiency and training speed. You can construct DMatrix from numpy.arrays

Parameters: data (string/numpy.array/scipy.sparse/pd.DataFrame/dt.Frame) – Data source of DMatrix. When data is string type, it represents the path libsvm format txt file, or binary file that xgboost can read from. label (list or numpy 1-D array, optional) – Label of the training data. missing (float, optional) – Value in the data which needs to be present as a missing value. If None, defaults to np.nan. weight (list or numpy 1-D array , optional) – Weight for each instance. Note For ranking task, weights are per-group. In ranking task, one weight is assigned to each group (not each data point). This is because we only care about the relative ordering of data points within each group, so it doesn’t make sense to assign weights to individual data points. silent (boolean, optional) – Whether print messages during construction feature_names (list, optional) – Set names for features. feature_types (list, optional) – Set types for features. nthread (integer, optional) – Number of threads to use for loading data from numpy array. If -1, uses maximum threads available on the system.
feature_names

Get feature names (column labels).

Returns: feature_names list or None
feature_types

Get feature types (column types).

Returns: feature_types list or None
get_base_margin()

Get the base margin of the DMatrix.

Returns: base_margin float
get_float_info(field)

Get float property from the DMatrix.

Parameters: field (str) – The field name of the information info – a numpy array of float information of the data array
get_label()

Get the label of the DMatrix.

Returns: label array
get_uint_info(field)

Get unsigned integer property from the DMatrix.

Parameters: field (str) – The field name of the information info – a numpy array of unsigned integer information of the data array
get_weight()

Get the weight of the DMatrix.

Returns: weight array
num_col()

Get the number of columns (features) in the DMatrix.

Returns: number of columns int
num_row()

Get the number of rows in the DMatrix.

Returns: number of rows int
save_binary(fname, silent=True)

Save DMatrix to an XGBoost buffer. Saved binary can be later loaded by providing the path to xgboost.DMatrix() as input.

Parameters: fname (string) – Name of the output buffer file. silent (bool (optional; default: True)) – If set, the output is suppressed.
set_base_margin(margin)

Set base margin of booster to start from.

This can be used to specify a prediction value of existing model to be base_margin However, remember margin is needed, instead of transformed prediction e.g. for logistic regression: need to put in value before logistic transformation see also example/demo.py

Parameters: margin (array like) – Prediction margin of each datapoint
set_float_info(field, data)

Set float type property into the DMatrix.

Parameters: field (str) – The field name of the information data (numpy array) – The array of data to be set
set_float_info_npy2d(field, data)
Set float type property into the DMatrix
for numpy 2d array input
Parameters: field (str) – The field name of the information data (numpy array) – The array of data to be set
set_group(group)

Set group size of DMatrix (used for ranking).

Parameters: group (array like) – Group size of each group
set_label(label)

Set label of dmatrix

Parameters: label (array like) – The label information to be set into DMatrix
set_label_npy2d(label)

Set label of dmatrix

Parameters: label (array like) – The label information to be set into DMatrix from numpy 2D array
set_uint_info(field, data)

Set uint type property into the DMatrix.

Parameters: field (str) – The field name of the information data (numpy array) – The array of data to be set
set_weight(weight)

Set weight of each instance.

Parameters: weight (array like) – Weight for each data point Note For ranking task, weights are per-group. In ranking task, one weight is assigned to each group (not each data point). This is because we only care about the relative ordering of data points within each group, so it doesn’t make sense to assign weights to individual data points.
set_weight_npy2d(weight)
Set weight of each instance
for numpy 2D array
Parameters: weight (array like) – Weight for each data point in numpy 2D array Note For ranking task, weights are per-group. In ranking task, one weight is assigned to each group (not each data point). This is because we only care about the relative ordering of data points within each group, so it doesn’t make sense to assign weights to individual data points.
slice(rindex)

Slice the DMatrix and return a new DMatrix that only contains rindex.

Parameters: rindex (list) – List of indices to be selected. res – A new DMatrix containing only selected indices. DMatrix
class xgboost.Booster(params=None, cache=(), model_file=None)

Bases: object

A Booster of XGBoost.

Booster is the model of xgboost, that contains low level routines for training, prediction and evaluation.

Parameters: params (dict) – Parameters for boosters. cache (list) – List of cache items. model_file (string) – Path to the model file.
attr(key)

Get attribute string from the Booster.

Parameters: key (str) – The key to get attribute from. value – The attribute value of the key, returns None if attribute do not exist. str
attributes()

Get attributes stored in the Booster as a dictionary.

Returns: result – Returns an empty dict if there’s no attributes. dictionary of attribute_name: attribute_value pairs of strings.
boost(dtrain, grad, hess)

Boost the booster for one iteration, with customized gradient statistics. Like xgboost.core.Booster.update(), this function should not be called directly by users.

Parameters: dtrain (DMatrix) – The training DMatrix. grad (list) – The first order of gradient. hess (list) – The second order of gradient.
copy()

Copy the booster object.

Returns: booster – a copied booster model Booster
dump_model(fout, fmap='', with_stats=False, dump_format='text')

Dump model into a text or JSON file.

Parameters: fout (string) – Output file name. fmap (string, optional) – Name of the file containing feature map names. with_stats (bool, optional) – Controls whether the split statistics are output. dump_format (string, optional) – Format of model dump file. Can be ‘text’ or ‘json’.
eval(data, name='eval', iteration=0)

Evaluate the model on mat.

Parameters: data (DMatrix) – The dmatrix storing the input. name (str, optional) – The name of the dataset. iteration (int, optional) – The current iteration number. result – Evaluation result string. str
eval_set(evals, iteration=0, feval=None)

Evaluate a set of data.

Parameters: evals (list of tuples (DMatrix, string)) – List of items to be evaluated. iteration (int) – Current iteration. feval (function) – Custom evaluation function. result – Evaluation result string. str
get_dump(fmap='', with_stats=False, dump_format='text')

Returns the model dump as a list of strings.

Parameters: fmap (string, optional) – Name of the file containing feature map names. with_stats (bool, optional) – Controls whether the split statistics are output. dump_format (string, optional) – Format of model dump. Can be ‘text’ or ‘json’.
get_fscore(fmap='')

Get feature importance of each feature.

Note

Feature importance is defined only for tree boosters

Feature importance is only defined when the decision tree model is chosen as base learner (booster=gbtree). It is not defined for other base learner types, such as linear learners (booster=gblinear).

Note

Zero-importance features will not be included

Keep in mind that this function does not include zero-importance feature, i.e. those features that have not been used in any split conditions.

Parameters: fmap (str (optional)) – The name of feature map file
get_score(fmap='', importance_type='weight')

Get feature importance of each feature. Importance type can be defined as:

• ‘weight’: the number of times a feature is used to split the data across all trees.
• ‘gain’: the average gain across all splits the feature is used in.
• ‘cover’: the average coverage across all splits the feature is used in.
• ‘total_gain’: the total gain across all splits the feature is used in.
• ‘total_cover’: the total coverage across all splits the feature is used in.

Note

Feature importance is defined only for tree boosters

Feature importance is only defined when the decision tree model is chosen as base learner (booster=gbtree). It is not defined for other base learner types, such as linear learners (booster=gblinear).

Parameters: fmap (str (optional)) – The name of feature map file. importance_type (str, default 'weight') – One of the importance types defined above.
get_split_value_histogram(feature, fmap='', bins=None, as_pandas=True)

Get split value histogram of a feature

Parameters: feature (str) – The name of the feature. fmap (str (optional)) – The name of feature map file. bin (int, default None) – The maximum number of bins. Number of bins equals number of unique split values n_unique, if bins == None or bins > n_unique. as_pandas (bool, default True) – Return pd.DataFrame when pandas is installed. If False or pandas is not installed, return numpy ndarray. a histogram of used splitting values for the specified feature either as numpy array or pandas DataFrame.
load_model(fname)

Load the model from a file.

The model is loaded from an XGBoost internal binary format which is universal among the various XGBoost interfaces. Auxiliary attributes of the Python Booster object (such as feature_names) will not be loaded. To preserve all attributes, pickle the Booster object.

Parameters: fname (string or a memory buffer) – Input file name or memory buffer(see also save_raw)
load_rabit_checkpoint()

Initialize the model by load from rabit checkpoint.

Returns: version – The version number of the model. integer
predict(data, output_margin=False, ntree_limit=0, pred_leaf=False, pred_contribs=False, approx_contribs=False, pred_interactions=False, validate_features=True)

Predict with data.

Note

This function is not thread safe.

For each booster object, predict can only be called from one thread. If you want to run prediction using multiple thread, call bst.copy() to make copies of model object and then call predict().

Note

Using predict() with DART booster

If the booster object is DART type, predict() will perform dropouts, i.e. only some of the trees will be evaluated. This will produce incorrect results if data is not the training data. To obtain correct results on test sets, set ntree_limit to a nonzero value, e.g.

preds = bst.predict(dtest, ntree_limit=num_round)

Parameters: data (DMatrix) – The dmatrix storing the input. output_margin (bool) – Whether to output the raw untransformed margin value. ntree_limit (int) – Limit number of trees in the prediction; defaults to 0 (use all trees). pred_leaf (bool) – When this option is on, the output will be a matrix of (nsample, ntrees) with each record indicating the predicted leaf index of each sample in each tree. Note that the leaf index of a tree is unique per tree, so you may find leaf 1 in both tree 1 and tree 0. pred_contribs (bool) – When this is True the output will be a matrix of size (nsample, nfeats + 1) with each record indicating the feature contributions (SHAP values) for that prediction. The sum of all feature contributions is equal to the raw untransformed margin value of the prediction. Note the final column is the bias term. approx_contribs (bool) – Approximate the contributions of each feature pred_interactions (bool) – When this is True the output will be a matrix of size (nsample, nfeats + 1, nfeats + 1) indicating the SHAP interaction values for each pair of features. The sum of each row (or column) of the interaction values equals the corresponding SHAP value (from pred_contribs), and the sum of the entire matrix equals the raw untransformed margin value of the prediction. Note the last row and column correspond to the bias term. validate_features (bool) – When this is True, validate that the Booster’s and data’s feature_names are identical. Otherwise, it is assumed that the feature_names are the same. prediction numpy array
save_model(fname)

Save the model to a file.

The model is saved in an XGBoost internal binary format which is universal among the various XGBoost interfaces. Auxiliary attributes of the Python Booster object (such as feature_names) will not be saved. To preserve all attributes, pickle the Booster object.

Parameters: fname (string) – Output file name
save_rabit_checkpoint()

Save the current booster to rabit checkpoint.

save_raw()

Save the model to a in memory buffer representation

Returns: a in memory buffer representation of the model
set_attr(**kwargs)

Set the attribute of the Booster.

Parameters: **kwargs – The attributes to set. Setting a value to None deletes an attribute.
set_param(params, value=None)

Set parameters into the Booster.

Parameters: params (dict/list/str) – list of key,value pairs, dict of key to value or simply str key value (optional) – value of the specified parameter, when params is str key
trees_to_dataframe(fmap='')

Parse a boosted tree model text dump into a pandas DataFrame structure.

This feature is only defined when the decision tree model is chosen as base learner (booster in {gbtree, dart}). It is not defined for other base learner types, such as linear learners (booster=gblinear).

Parameters: fmap (str (optional)) – The name of feature map file.
update(dtrain, iteration, fobj=None)

Update for one iteration, with objective function calculated internally. This function should not be called directly by users.

Parameters: dtrain (DMatrix) – Training data. iteration (int) – Current iteration number. fobj (function) – Customized objective function.

## Learning API¶

Training Library containing training routines.

xgboost.train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None, maximize=False, early_stopping_rounds=None, evals_result=None, verbose_eval=True, xgb_model=None, callbacks=None, learning_rates=None)

Train a booster with given parameters.

Parameters: params (dict) – Booster params. dtrain (DMatrix) – Data to be trained. num_boost_round (int) – Number of boosting iterations. evals (list of pairs (DMatrix, string)) – List of items to be evaluated during training, this allows user to watch performance on the validation set. obj (function) – Customized objective function. feval (function) – Customized evaluation function. maximize (bool) – Whether to maximize feval. early_stopping_rounds (int) – Activates early stopping. Validation error needs to decrease at least every early_stopping_rounds round(s) to continue training. Requires at least one item in evals. If there’s more than one, will use the last. Returns the model from the last iteration (not the best one). If early stopping occurs, the model will have three additional fields: bst.best_score, bst.best_iteration and bst.best_ntree_limit. (Use bst.best_ntree_limit to get the correct value if num_parallel_tree and/or num_class appears in the parameters) evals_result (dict) – This dictionary stores the evaluation results of all the items in watchlist. Example: with a watchlist containing [(dtest,'eval'), (dtrain,'train')] and a parameter containing ('eval_metric': 'logloss'), the evals_result returns {'train': {'logloss': ['0.48253', '0.35953']}, 'eval': {'logloss': ['0.480385', '0.357756']}}  verbose_eval (bool or int) – Requires at least one item in evals. If verbose_eval is True then the evaluation metric on the validation set is printed at each boosting stage. If verbose_eval is an integer then the evaluation metric on the validation set is printed at every given verbose_eval boosting stage. The last boosting stage / the boosting stage found by using early_stopping_rounds is also printed. Example: with verbose_eval=4 and at least one item in evals, an evaluation metric is printed every 4 boosting stages, instead of every boosting stage. learning_rates (list or function (deprecated - use callback API instead)) – List of learning rate for each boosting round or a customized function that calculates eta in terms of current number of round and the total number of boosting round (e.g. yields learning rate decay) xgb_model (file name of stored xgb model or 'Booster' instance) – Xgb model to be loaded before training (allows training continuation). callbacks (list of callback functions) – List of callback functions that are applied at end of each iteration. It is possible to use predefined callbacks by using Callback API. Example: [xgb.callback.reset_learning_rate(custom_rates)]  Booster a trained booster model
xgboost.cv(params, dtrain, num_boost_round=10, nfold=3, stratified=False, folds=None, metrics=(), obj=None, feval=None, maximize=False, early_stopping_rounds=None, fpreproc=None, as_pandas=True, verbose_eval=None, show_stdv=True, seed=0, callbacks=None, shuffle=True)

Cross-validation with given parameters.

Parameters: params (dict) – Booster params. dtrain (DMatrix) – Data to be trained. num_boost_round (int) – Number of boosting iterations. nfold (int) – Number of folds in CV. stratified (bool) – Perform stratified sampling. folds (a KFold or StratifiedKFold instance or list of fold indices) – Sklearn KFolds or StratifiedKFolds object. Alternatively may explicitly pass sample indices for each fold. For n folds, folds should be a length n list of tuples. Each tuple is (in,out) where in is a list of indices to be used as the training samples for the n th fold and out is a list of indices to be used as the testing samples for the n th fold. metrics (string or list of strings) – Evaluation metrics to be watched in CV. obj (function) – Custom objective function. feval (function) – Custom evaluation function. maximize (bool) – Whether to maximize feval. early_stopping_rounds (int) – Activates early stopping. CV error needs to decrease at least every round(s) to continue. Last entry in evaluation history is the one from best iteration. fpreproc (function) – Preprocessing function that takes (dtrain, dtest, param) and returns transformed versions of those. as_pandas (bool, default True) – Return pd.DataFrame when pandas is installed. If False or pandas is not installed, return np.ndarray verbose_eval (bool, int, or None, default None) – Whether to display the progress. If None, progress will be displayed when np.ndarray is returned. If True, progress will be displayed at boosting stage. If an integer is given, progress will be displayed at every given verbose_eval boosting stage. show_stdv (bool, default True) – Whether to display the standard deviation in progress. Results are not affected, and always contains std. seed (int) – Seed used to generate the folds (passed to numpy.random.seed). callbacks (list of callback functions) – List of callback functions that are applied at end of each iteration. It is possible to use predefined callbacks by using Callback API. Example: [xgb.callback.reset_learning_rate(custom_rates)]  shuffle (bool) – Shuffle data before creating folds. evaluation history list(string)

## Scikit-Learn API¶

Scikit-Learn Wrapper interface for XGBoost.

class xgboost.XGBRegressor(max_depth=3, learning_rate=0.1, n_estimators=100, verbosity=1, silent=None, objective='reg:linear', booster='gbtree', n_jobs=1, nthread=None, gamma=0, min_child_weight=1, max_delta_step=0, subsample=1, colsample_bytree=1, colsample_bylevel=1, colsample_bynode=1, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, base_score=0.5, random_state=0, seed=None, missing=None, importance_type='gain', **kwargs)

Bases: xgboost.sklearn.XGBModel, object

Implementation of the scikit-learn API for XGBoost regression.

Parameters: max_depth (int) – Maximum tree depth for base learners. learning_rate (float) – Boosting learning rate (xgb’s “eta”) n_estimators (int) – Number of trees to fit. verbosity (int) – The degree of verbosity. Valid values are 0 (silent) - 3 (debug). silent (boolean) – Whether to print messages while running boosting. Deprecated. Use verbosity instead. objective (string or callable) – Specify the learning task and the corresponding learning objective or a custom objective function to be used (see note below). booster (string) – Specify which booster to use: gbtree, gblinear or dart. nthread (int) – Number of parallel threads used to run xgboost. (Deprecated, please use n_jobs) n_jobs (int) – Number of parallel threads used to run xgboost. (replaces nthread) gamma (float) – Minimum loss reduction required to make a further partition on a leaf node of the tree. min_child_weight (int) – Minimum sum of instance weight(hessian) needed in a child. max_delta_step (int) – Maximum delta step we allow each tree’s weight estimation to be. subsample (float) – Subsample ratio of the training instance. colsample_bytree (float) – Subsample ratio of columns when constructing each tree. colsample_bylevel (float) – Subsample ratio of columns for each level. colsample_bynode (float) – Subsample ratio of columns for each split. reg_alpha (float (xgb's alpha)) – L1 regularization term on weights reg_lambda (float (xgb's lambda)) – L2 regularization term on weights scale_pos_weight (float) – Balancing of positive and negative weights. base_score – The initial prediction score of all instances, global bias. seed (int) – Random number seed. (Deprecated, please use random_state) random_state (int) – Random number seed. (replaces seed) missing (float, optional) – Value in the data which needs to be present as a missing value. If None, defaults to np.nan. importance_type (string, default "gain") – The feature importance type for the feature_importances_ property: either “gain”, “weight”, “cover”, “total_gain” or “total_cover”. **kwargs (dict, optional) – Keyword arguments for XGBoost Booster object. Full documentation of parameters can be found here: https://github.com/dmlc/xgboost/blob/master/doc/parameter.rst. Attempting to set a parameter via the constructor args and **kwargs dict simultaneously will result in a TypeError. Note **kwargs unsupported by scikit-learn **kwargs is unsupported by scikit-learn. We do not guarantee that parameters passed via this argument will interact properly with scikit-learn.

Note

A custom objective function can be provided for the objective parameter. In this case, it should have the signature objective(y_true, y_pred) -> grad, hess:

y_true: array_like of shape [n_samples]
The target values
y_pred: array_like of shape [n_samples]
The predicted values
The value of the gradient for each sample point.
hess: array_like of shape [n_samples]
The value of the second derivative for each sample point
apply(X, ntree_limit=0)

Return the predicted leaf every tree for each sample.

Parameters: X (array_like, shape=[n_samples, n_features]) – Input features matrix. ntree_limit (int) – Limit number of trees in the prediction; defaults to 0 (use all trees). X_leaves – For each datapoint x in X and for each tree, return the index of the leaf x ends up in. Leaves are numbered within [0; 2**(self.max_depth+1)), possibly with gaps in the numbering. array_like, shape=[n_samples, n_trees]
coef_

Coefficients property

Note

Coefficients are defined only for linear learners

Coefficients are only defined when the linear model is chosen as base learner (booster=gblinear). It is not defined for other base learner types, such as tree learners (booster=gbtree).

Returns: coef_ array of shape [n_features] or [n_classes, n_features]
evals_result()

Return the evaluation results.

If eval_set is passed to the fit function, you can call evals_result() to get evaluation results for all passed eval_sets. When eval_metric is also passed to the fit function, the evals_result will contain the eval_metrics passed to the fit function.

Returns: evals_result dictionary

Example

param_dist = {'objective':'binary:logistic', 'n_estimators':2}

clf = xgb.XGBModel(**param_dist)

clf.fit(X_train, y_train,
eval_set=[(X_train, y_train), (X_test, y_test)],
eval_metric='logloss',
verbose=True)

evals_result = clf.evals_result()


The variable evals_result will contain:

{'validation_0': {'logloss': ['0.604835', '0.531479']},
'validation_1': {'logloss': ['0.41965', '0.17686']}}

feature_importances_

Feature importances property

Note

Feature importance is defined only for tree boosters

Feature importance is only defined when the decision tree model is chosen as base learner (booster=gbtree). It is not defined for other base learner types, such as linear learners (booster=gblinear).

Returns: feature_importances_ array of shape [n_features]
fit(X, y, sample_weight=None, eval_set=None, eval_metric=None, early_stopping_rounds=None, verbose=True, xgb_model=None, sample_weight_eval_set=None, callbacks=None)

Parameters: X (array_like) – Feature matrix y (array_like) – Labels sample_weight (array_like) – instance weights eval_set (list, optional) – A list of (X, y) tuple pairs to use as a validation set for early-stopping sample_weight_eval_set (list, optional) – A list of the form [L_1, L_2, …, L_n], where each L_i is a list of instance weights on the i-th validation set. eval_metric (str, callable, optional) – If a str, should be a built-in evaluation metric to use. See doc/parameter.rst. If callable, a custom evaluation metric. The call signature is func(y_predicted, y_true) where y_true will be a DMatrix object such that you may need to call the get_label method. It must return a str, value pair where the str is a name for the evaluation and value is the value of the evaluation function. This objective is always minimized. early_stopping_rounds (int) – Activates early stopping. Validation error needs to decrease at least every round(s) to continue training. Requires at least one item in evals. If there’s more than one, will use the last. Returns the model from the last iteration (not the best one). If early stopping occurs, the model will have three additional fields: bst.best_score, bst.best_iteration and bst.best_ntree_limit. (Use bst.best_ntree_limit to get the correct value if num_parallel_tree and/or num_class appears in the parameters) verbose (bool) – If verbose and an evaluation set is used, writes the evaluation metric measured on the validation set to stderr. xgb_model (str) – file name of stored xgb model or ‘Booster’ instance Xgb model to be loaded before training (allows training continuation). callbacks (list of callback functions) – List of callback functions that are applied at end of each iteration. It is possible to use predefined callbacks by using Callback API. Example: [xgb.callback.reset_learning_rate(custom_rates)] 
get_booster()

Get the underlying xgboost Booster of this model.

This will raise an exception when fit was not called

Returns: booster a xgboost booster of underlying model
get_num_boosting_rounds()

Gets the number of xgboost boosting rounds.

get_params(deep=False)

Get parameters.

get_xgb_params()

Get xgboost type parameters.

intercept_

Intercept (bias) property

Note

Intercept is defined only for linear learners

Intercept (bias) is only defined when the linear model is chosen as base learner (booster=gblinear). It is not defined for other base learner types, such as tree learners (booster=gbtree).

Returns: intercept_ array of shape (1,) or [n_classes]
load_model(fname)

Load the model from a file.

The model is loaded from an XGBoost internal binary format which is universal among the various XGBoost interfaces. Auxiliary attributes of the Python Booster object (such as feature names) will not be loaded. Label encodings (text labels to numeric labels) will be also lost. If you are using only the Python interface, we recommend pickling the model object for best results.

Parameters: fname (string or a memory buffer) – Input file name or memory buffer(see also save_raw)
predict(data, output_margin=False, ntree_limit=None, validate_features=True)

Predict with data.

Note

This function is not thread safe.

For each booster object, predict can only be called from one thread. If you want to run prediction using multiple thread, call xgb.copy() to make copies of model object and then call predict().

Note

Using predict() with DART booster

If the booster object is DART type, predict() will perform dropouts, i.e. only some of the trees will be evaluated. This will produce incorrect results if data is not the training data. To obtain correct results on test sets, set ntree_limit to a nonzero value, e.g.

preds = bst.predict(dtest, ntree_limit=num_round)

Parameters: data (DMatrix) – The dmatrix storing the input. output_margin (bool) – Whether to output the raw untransformed margin value. ntree_limit (int) – Limit number of trees in the prediction; defaults to best_ntree_limit if defined (i.e. it has been trained with early stopping), otherwise 0 (use all trees). validate_features (bool) – When this is True, validate that the Booster’s and data’s feature_names are identical. Otherwise, it is assumed that the feature_names are the same. prediction numpy array
save_model(fname)

Save the model to a file.

The model is saved in an XGBoost internal binary format which is universal among the various XGBoost interfaces. Auxiliary attributes of the Python Booster object (such as feature names) will not be loaded. Label encodings (text labels to numeric labels) will be also lost. If you are using only the Python interface, we recommend pickling the model object for best results.

Parameters: fname (string) – Output file name
set_params(**params)

Set the parameters of this estimator. Modification of the sklearn method to allow unknown kwargs. This allows using the full range of xgboost parameters that are not defined as member variables in sklearn grid search. :returns: :rtype: self

class xgboost.XGBClassifier(max_depth=3, learning_rate=0.1, n_estimators=100, verbosity=1, silent=None, objective='binary:logistic', booster='gbtree', n_jobs=1, nthread=None, gamma=0, min_child_weight=1, max_delta_step=0, subsample=1, colsample_bytree=1, colsample_bylevel=1, colsample_bynode=1, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, base_score=0.5, random_state=0, seed=None, missing=None, **kwargs)

Bases: xgboost.sklearn.XGBModel, object

Implementation of the scikit-learn API for XGBoost classification.

Parameters: max_depth (int) – Maximum tree depth for base learners. learning_rate (float) – Boosting learning rate (xgb’s “eta”) n_estimators (int) – Number of trees to fit. verbosity (int) – The degree of verbosity. Valid values are 0 (silent) - 3 (debug). silent (boolean) – Whether to print messages while running boosting. Deprecated. Use verbosity instead. objective (string or callable) – Specify the learning task and the corresponding learning objective or a custom objective function to be used (see note below). booster (string) – Specify which booster to use: gbtree, gblinear or dart. nthread (int) – Number of parallel threads used to run xgboost. (Deprecated, please use n_jobs) n_jobs (int) – Number of parallel threads used to run xgboost. (replaces nthread) gamma (float) – Minimum loss reduction required to make a further partition on a leaf node of the tree. min_child_weight (int) – Minimum sum of instance weight(hessian) needed in a child. max_delta_step (int) – Maximum delta step we allow each tree’s weight estimation to be. subsample (float) – Subsample ratio of the training instance. colsample_bytree (float) – Subsample ratio of columns when constructing each tree. colsample_bylevel (float) – Subsample ratio of columns for each level. colsample_bynode (float) – Subsample ratio of columns for each split. reg_alpha (float (xgb's alpha)) – L1 regularization term on weights reg_lambda (float (xgb's lambda)) – L2 regularization term on weights scale_pos_weight (float) – Balancing of positive and negative weights. base_score – The initial prediction score of all instances, global bias. seed (int) – Random number seed. (Deprecated, please use random_state) random_state (int) – Random number seed. (replaces seed) missing (float, optional) – Value in the data which needs to be present as a missing value. If None, defaults to np.nan. importance_type (string, default "gain") – The feature importance type for the feature_importances_ property: either “gain”, “weight”, “cover”, “total_gain” or “total_cover”. **kwargs (dict, optional) – Keyword arguments for XGBoost Booster object. Full documentation of parameters can be found here: https://github.com/dmlc/xgboost/blob/master/doc/parameter.rst. Attempting to set a parameter via the constructor args and **kwargs dict simultaneously will result in a TypeError. Note **kwargs unsupported by scikit-learn **kwargs is unsupported by scikit-learn. We do not guarantee that parameters passed via this argument will interact properly with scikit-learn.

Note

A custom objective function can be provided for the objective parameter. In this case, it should have the signature objective(y_true, y_pred) -> grad, hess:

y_true: array_like of shape [n_samples]
The target values
y_pred: array_like of shape [n_samples]
The predicted values
The value of the gradient for each sample point.
hess: array_like of shape [n_samples]
The value of the second derivative for each sample point
apply(X, ntree_limit=0)

Return the predicted leaf every tree for each sample.

Parameters: X (array_like, shape=[n_samples, n_features]) – Input features matrix. ntree_limit (int) – Limit number of trees in the prediction; defaults to 0 (use all trees). X_leaves – For each datapoint x in X and for each tree, return the index of the leaf x ends up in. Leaves are numbered within [0; 2**(self.max_depth+1)), possibly with gaps in the numbering. array_like, shape=[n_samples, n_trees]
coef_

Coefficients property

Note

Coefficients are defined only for linear learners

Coefficients are only defined when the linear model is chosen as base learner (booster=gblinear). It is not defined for other base learner types, such as tree learners (booster=gbtree).

Returns: coef_ array of shape [n_features] or [n_classes, n_features]
evals_result()

Return the evaluation results.

If eval_set is passed to the fit function, you can call evals_result() to get evaluation results for all passed eval_sets. When eval_metric is also passed to the fit function, the evals_result will contain the eval_metrics passed to the fit function.

Returns: evals_result dictionary

Example

param_dist = {'objective':'binary:logistic', 'n_estimators':2}

clf = xgb.XGBClassifier(**param_dist)

clf.fit(X_train, y_train,
eval_set=[(X_train, y_train), (X_test, y_test)],
eval_metric='logloss',
verbose=True)

evals_result = clf.evals_result()


The variable evals_result will contain

{'validation_0': {'logloss': ['0.604835', '0.531479']},
'validation_1': {'logloss': ['0.41965', '0.17686']}}

feature_importances_

Feature importances property

Note

Feature importance is defined only for tree boosters

Feature importance is only defined when the decision tree model is chosen as base learner (booster=gbtree). It is not defined for other base learner types, such as linear learners (booster=gblinear).

Returns: feature_importances_ array of shape [n_features]
fit(X, y, sample_weight=None, eval_set=None, eval_metric=None, early_stopping_rounds=None, verbose=True, xgb_model=None, sample_weight_eval_set=None, callbacks=None)

Parameters: X (array_like) – Feature matrix y (array_like) – Labels sample_weight (array_like) – Weight for each instance eval_set (list, optional) – A list of (X, y) pairs to use as a validation set for early-stopping sample_weight_eval_set (list, optional) – A list of the form [L_1, L_2, …, L_n], where each L_i is a list of instance weights on the i-th validation set. eval_metric (str, callable, optional) – If a str, should be a built-in evaluation metric to use. See doc/parameter.rst. If callable, a custom evaluation metric. The call signature is func(y_predicted, y_true) where y_true will be a DMatrix object such that you may need to call the get_label method. It must return a str, value pair where the str is a name for the evaluation and value is the value of the evaluation function. This objective is always minimized. early_stopping_rounds (int, optional) – Activates early stopping. Validation error needs to decrease at least every round(s) to continue training. Requires at least one item in evals. If there’s more than one, will use the last. If early stopping occurs, the model will have three additional fields: bst.best_score, bst.best_iteration and bst.best_ntree_limit (bst.best_ntree_limit is the ntree_limit parameter default value in predict method if not any other value is specified). (Use bst.best_ntree_limit to get the correct value if num_parallel_tree and/or num_class appears in the parameters) verbose (bool) – If verbose and an evaluation set is used, writes the evaluation metric measured on the validation set to stderr. xgb_model (str) – file name of stored xgb model or ‘Booster’ instance Xgb model to be loaded before training (allows training continuation). callbacks (list of callback functions) – List of callback functions that are applied at end of each iteration. It is possible to use predefined callbacks by using Callback API. Example: [xgb.callback.reset_learning_rate(custom_rates)] 
get_booster()

Get the underlying xgboost Booster of this model.

This will raise an exception when fit was not called

Returns: booster a xgboost booster of underlying model
get_num_boosting_rounds()

Gets the number of xgboost boosting rounds.

get_params(deep=False)

Get parameters.

get_xgb_params()

Get xgboost type parameters.

intercept_

Intercept (bias) property

Note

Intercept is defined only for linear learners

Intercept (bias) is only defined when the linear model is chosen as base learner (booster=gblinear). It is not defined for other base learner types, such as tree learners (booster=gbtree).

Returns: intercept_ array of shape (1,) or [n_classes]
load_model(fname)

Load the model from a file.

The model is loaded from an XGBoost internal binary format which is universal among the various XGBoost interfaces. Auxiliary attributes of the Python Booster object (such as feature names) will not be loaded. Label encodings (text labels to numeric labels) will be also lost. If you are using only the Python interface, we recommend pickling the model object for best results.

Parameters: fname (string or a memory buffer) – Input file name or memory buffer(see also save_raw)
predict(data, output_margin=False, ntree_limit=None, validate_features=True)

Predict with data.

Note

This function is not thread safe.

For each booster object, predict can only be called from one thread. If you want to run prediction using multiple thread, call xgb.copy() to make copies of model object and then call predict().

Note

Using predict() with DART booster

If the booster object is DART type, predict() will perform dropouts, i.e. only some of the trees will be evaluated. This will produce incorrect results if data is not the training data. To obtain correct results on test sets, set ntree_limit to a nonzero value, e.g.

preds = bst.predict(dtest, ntree_limit=num_round)

Parameters: data (DMatrix) – The dmatrix storing the input. output_margin (bool) – Whether to output the raw untransformed margin value. ntree_limit (int) – Limit number of trees in the prediction; defaults to best_ntree_limit if defined (i.e. it has been trained with early stopping), otherwise 0 (use all trees). validate_features (bool) – When this is True, validate that the Booster’s and data’s feature_names are identical. Otherwise, it is assumed that the feature_names are the same. prediction numpy array
predict_proba(data, ntree_limit=None, validate_features=True)

Predict the probability of each data example being of a given class.

Note

This function is not thread safe

For each booster object, predict can only be called from one thread. If you want to run prediction using multiple thread, call xgb.copy() to make copies of model object and then call predict

Parameters: data (DMatrix) – The dmatrix storing the input. ntree_limit (int) – Limit number of trees in the prediction; defaults to best_ntree_limit if defined (i.e. it has been trained with early stopping), otherwise 0 (use all trees). validate_features (bool) – When this is True, validate that the Booster’s and data’s feature_names are identical. Otherwise, it is assumed that the feature_names are the same. prediction – a numpy array with the probability of each data example being of a given class. numpy array
save_model(fname)

Save the model to a file.

The model is saved in an XGBoost internal binary format which is universal among the various XGBoost interfaces. Auxiliary attributes of the Python Booster object (such as feature names) will not be loaded. Label encodings (text labels to numeric labels) will be also lost. If you are using only the Python interface, we recommend pickling the model object for best results.

Parameters: fname (string) – Output file name
set_params(**params)

Set the parameters of this estimator. Modification of the sklearn method to allow unknown kwargs. This allows using the full range of xgboost parameters that are not defined as member variables in sklearn grid search. :returns: :rtype: self

class xgboost.XGBRanker(max_depth=3, learning_rate=0.1, n_estimators=100, verbosity=1, silent=None, objective='rank:pairwise', booster='gbtree', n_jobs=-1, nthread=None, gamma=0, min_child_weight=1, max_delta_step=0, subsample=1, colsample_bytree=1, colsample_bylevel=1, colsample_bynode=1, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, base_score=0.5, random_state=0, seed=None, missing=None, **kwargs)

Bases: xgboost.sklearn.XGBModel

Implementation of the Scikit-Learn API for XGBoost Ranking.

Parameters: max_depth (int) – Maximum tree depth for base learners. learning_rate (float) – Boosting learning rate (xgb’s “eta”) n_estimators (int) – Number of boosted trees to fit. verbosity (int) – The degree of verbosity. Valid values are 0 (silent) - 3 (debug). silent (boolean) – Whether to print messages while running boosting. Deprecated. Use verbosity instead. objective (string) – Specify the learning task and the corresponding learning objective. The objective name must start with “rank:”. booster (string) – Specify which booster to use: gbtree, gblinear or dart. nthread (int) – Number of parallel threads used to run xgboost. (Deprecated, please use n_jobs) n_jobs (int) – Number of parallel threads used to run xgboost. (replaces nthread) gamma (float) – Minimum loss reduction required to make a further partition on a leaf node of the tree. min_child_weight (int) – Minimum sum of instance weight(hessian) needed in a child. max_delta_step (int) – Maximum delta step we allow each tree’s weight estimation to be. subsample (float) – Subsample ratio of the training instance. colsample_bytree (float) – Subsample ratio of columns when constructing each tree. colsample_bylevel (float) – Subsample ratio of columns for each level. colsample_bynode (float) – Subsample ratio of columns for each split. reg_alpha (float (xgb's alpha)) – L1 regularization term on weights reg_lambda (float (xgb's lambda)) – L2 regularization term on weights scale_pos_weight (float) – Balancing of positive and negative weights. base_score – The initial prediction score of all instances, global bias. seed (int) – Random number seed. (Deprecated, please use random_state) random_state (int) – Random number seed. (replaces seed) missing (float, optional) – Value in the data which needs to be present as a missing value. If None, defaults to np.nan. **kwargs (dict, optional) – Keyword arguments for XGBoost Booster object. Full documentation of parameters can be found here: https://github.com/dmlc/xgboost/blob/master/doc/parameter.rst. Attempting to set a parameter via the constructor args and **kwargs dict simultaneously will result in a TypeError. Note **kwargs unsupported by scikit-learn **kwargs is unsupported by scikit-learn. We do not guarantee that parameters passed via this argument will interact properly with scikit-learn.

Note

A custom objective function is currently not supported by XGBRanker.

Note

Group information is required for ranking tasks.

Before fitting the model, your data need to be sorted by group. When fitting the model, you need to provide an additional array that contains the size of each group.

For example, if your original data look like:

 qid label features 1 0 x_1 1 1 x_2 1 0 x_3 2 0 x_4 2 1 x_5 2 1 x_6 2 1 x_7

then your group array should be [3, 4].

apply(X, ntree_limit=0)

Return the predicted leaf every tree for each sample.

Parameters: X (array_like, shape=[n_samples, n_features]) – Input features matrix. ntree_limit (int) – Limit number of trees in the prediction; defaults to 0 (use all trees). X_leaves – For each datapoint x in X and for each tree, return the index of the leaf x ends up in. Leaves are numbered within [0; 2**(self.max_depth+1)), possibly with gaps in the numbering. array_like, shape=[n_samples, n_trees]
coef_

Coefficients property

Note

Coefficients are defined only for linear learners

Coefficients are only defined when the linear model is chosen as base learner (booster=gblinear). It is not defined for other base learner types, such as tree learners (booster=gbtree).

Returns: coef_ array of shape [n_features] or [n_classes, n_features]
evals_result()

Return the evaluation results.

If eval_set is passed to the fit function, you can call evals_result() to get evaluation results for all passed eval_sets. When eval_metric is also passed to the fit function, the evals_result will contain the eval_metrics passed to the fit function.

Returns: evals_result dictionary

Example

param_dist = {'objective':'binary:logistic', 'n_estimators':2}

clf = xgb.XGBModel(**param_dist)

clf.fit(X_train, y_train,
eval_set=[(X_train, y_train), (X_test, y_test)],
eval_metric='logloss',
verbose=True)

evals_result = clf.evals_result()


The variable evals_result will contain:

{'validation_0': {'logloss': ['0.604835', '0.531479']},
'validation_1': {'logloss': ['0.41965', '0.17686']}}

feature_importances_

Feature importances property

Note

Feature importance is defined only for tree boosters

Feature importance is only defined when the decision tree model is chosen as base learner (booster=gbtree). It is not defined for other base learner types, such as linear learners (booster=gblinear).

Returns: feature_importances_ array of shape [n_features]
fit(X, y, group, sample_weight=None, eval_set=None, sample_weight_eval_set=None, eval_group=None, eval_metric=None, early_stopping_rounds=None, verbose=False, xgb_model=None, callbacks=None)

Parameters: X (array_like) – Feature matrix y (array_like) – Labels group (array_like) – group size of training data sample_weight (array_like) – group weights Note Weights are per-group for ranking tasks In ranking task, one weight is assigned to each group (not each data point). This is because we only care about the relative ordering of data points within each group, so it doesn’t make sense to assign weights to individual data points. eval_set (list, optional) – A list of (X, y) tuple pairs to use as a validation set for early-stopping sample_weight_eval_set (list, optional) – A list of the form [L_1, L_2, …, L_n], where each L_i is a list of group weights on the i-th validation set. Note Weights are per-group for ranking tasks In ranking task, one weight is assigned to each group (not each data point). This is because we only care about the relative ordering of data points within each group, so it doesn’t make sense to assign weights to individual data points. eval_group (list of arrays, optional) – A list that contains the group size corresponds to each (X, y) pair in eval_set eval_metric (str, callable, optional) – If a str, should be a built-in evaluation metric to use. See doc/parameter.rst. If callable, a custom evaluation metric. The call signature is func(y_predicted, y_true) where y_true will be a DMatrix object such that you may need to call the get_label method. It must return a str, value pair where the str is a name for the evaluation and value is the value of the evaluation function. This objective is always minimized. early_stopping_rounds (int) – Activates early stopping. Validation error needs to decrease at least every round(s) to continue training. Requires at least one item in evals. If there’s more than one, will use the last. Returns the model from the last iteration (not the best one). If early stopping occurs, the model will have three additional fields: bst.best_score, bst.best_iteration and bst.best_ntree_limit. (Use bst.best_ntree_limit to get the correct value if num_parallel_tree and/or num_class appears in the parameters) verbose (bool) – If verbose and an evaluation set is used, writes the evaluation metric measured on the validation set to stderr. xgb_model (str) – file name of stored xgb model or ‘Booster’ instance Xgb model to be loaded before training (allows training continuation). callbacks (list of callback functions) – List of callback functions that are applied at end of each iteration. It is possible to use predefined callbacks by using Callback API. Example: [xgb.callback.reset_learning_rate(custom_rates)] 
get_booster()

Get the underlying xgboost Booster of this model.

This will raise an exception when fit was not called

Returns: booster a xgboost booster of underlying model
get_num_boosting_rounds()

Gets the number of xgboost boosting rounds.

get_params(deep=False)

Get parameters.

get_xgb_params()

Get xgboost type parameters.

intercept_

Intercept (bias) property

Note

Intercept is defined only for linear learners

Intercept (bias) is only defined when the linear model is chosen as base learner (booster=gblinear). It is not defined for other base learner types, such as tree learners (booster=gbtree).

Returns: intercept_ array of shape (1,) or [n_classes]
load_model(fname)

Load the model from a file.

The model is loaded from an XGBoost internal binary format which is universal among the various XGBoost interfaces. Auxiliary attributes of the Python Booster object (such as feature names) will not be loaded. Label encodings (text labels to numeric labels) will be also lost. If you are using only the Python interface, we recommend pickling the model object for best results.

Parameters: fname (string or a memory buffer) – Input file name or memory buffer(see also save_raw)
predict(data, output_margin=False, ntree_limit=0, validate_features=True)

Predict with data.

Note

This function is not thread safe.

For each booster object, predict can only be called from one thread. If you want to run prediction using multiple thread, call xgb.copy() to make copies of model object and then call predict().

Note

Using predict() with DART booster

If the booster object is DART type, predict() will perform dropouts, i.e. only some of the trees will be evaluated. This will produce incorrect results if data is not the training data. To obtain correct results on test sets, set ntree_limit to a nonzero value, e.g.

preds = bst.predict(dtest, ntree_limit=num_round)

Parameters: data (DMatrix) – The dmatrix storing the input. output_margin (bool) – Whether to output the raw untransformed margin value. ntree_limit (int) – Limit number of trees in the prediction; defaults to best_ntree_limit if defined (i.e. it has been trained with early stopping), otherwise 0 (use all trees). validate_features (bool) – When this is True, validate that the Booster’s and data’s feature_names are identical. Otherwise, it is assumed that the feature_names are the same. prediction numpy array
save_model(fname)

Save the model to a file.

The model is saved in an XGBoost internal binary format which is universal among the various XGBoost interfaces. Auxiliary attributes of the Python Booster object (such as feature names) will not be loaded. Label encodings (text labels to numeric labels) will be also lost. If you are using only the Python interface, we recommend pickling the model object for best results.

Parameters: fname (string) – Output file name
set_params(**params)

Set the parameters of this estimator. Modification of the sklearn method to allow unknown kwargs. This allows using the full range of xgboost parameters that are not defined as member variables in sklearn grid search. :returns: :rtype: self

## Plotting API¶

Plotting Library.

xgboost.plot_importance(booster, ax=None, height=0.2, xlim=None, ylim=None, title='Feature importance', xlabel='F score', ylabel='Features', importance_type='weight', max_num_features=None, grid=True, show_values=True, **kwargs)

Plot importance based on fitted trees.

Parameters: booster (Booster, XGBModel or dict) – Booster or XGBModel instance, or dict taken by Booster.get_fscore() ax (matplotlib Axes, default None) – Target axes instance. If None, new figure and axes will be created. grid (bool, Turn the axes grids on or off. Default is True (On)) – importance_type (str, default "weight") – How the importance is calculated: either “weight”, “gain”, or “cover” ”weight” is the number of times a feature appears in a tree ”gain” is the average gain of splits which use the feature ”cover” is the average coverage of splits which use the feature where coverage is defined as the number of samples affected by the split max_num_features (int, default None) – Maximum number of top features displayed on plot. If None, all features will be displayed. height (float, default 0.2) – Bar height, passed to ax.barh() xlim (tuple, default None) – Tuple passed to axes.xlim() ylim (tuple, default None) – Tuple passed to axes.ylim() title (str, default "Feature importance") – Axes title. To disable, pass None. xlabel (str, default "F score") – X axis title label. To disable, pass None. ylabel (str, default "Features") – Y axis title label. To disable, pass None. show_values (bool, default True) – Show values on plot. To disable, pass False. kwargs – Other keywords passed to ax.barh() ax matplotlib Axes
xgboost.plot_tree(booster, fmap='', num_trees=0, rankdir='UT', ax=None, **kwargs)

Plot specified tree.

Parameters: booster (Booster, XGBModel) – Booster or XGBModel instance fmap (str (optional)) – The name of feature map file num_trees (int, default 0) – Specify the ordinal number of target tree rankdir (str, default "UT") – Passed to graphiz via graph_attr ax (matplotlib Axes, default None) – Target axes instance. If None, new figure and axes will be created. kwargs – Other keywords passed to to_graphviz ax matplotlib Axes
xgboost.to_graphviz(booster, fmap='', num_trees=0, rankdir='UT', yes_color='#0000FF', no_color='#FF0000', condition_node_params=None, leaf_node_params=None, **kwargs)

Convert specified tree to graphviz instance. IPython can automatically plot the returned graphiz instance. Otherwise, you should call .render() method of the returned graphiz instance.

Parameters: booster (Booster, XGBModel) – Booster or XGBModel instance fmap (str (optional)) – The name of feature map file num_trees (int, default 0) – Specify the ordinal number of target tree rankdir (str, default "UT") – Passed to graphiz via graph_attr yes_color (str, default '#0000FF') – Edge color when meets the node condition. no_color (str, default '#FF0000') – Edge color when doesn’t meet the node condition. condition_node_params (dict (optional)) – condition node configuration, {‘shape’:’box’, ’style’:’filled,rounded’, ‘fillcolor’:’#78bceb’ } leaf_node_params (dict (optional)) – leaf node configuration {‘shape’:’box’, ’style’:’filled’, ‘fillcolor’:’#e48038’ } kwargs – Other keywords passed to graphviz graph_attr ax matplotlib Axes

## Callback API¶

xgboost.callback.print_evaluation(period=1, show_stdv=True)

Create a callback that print evaluation result.

We print the evaluation results every period iterations and on the first and the last iterations.

Parameters: period (int) – The period to log the evaluation results show_stdv (bool, optional) – Whether show stdv if provided callback – A callback that print evaluation every period iterations. function
xgboost.callback.record_evaluation(eval_result)

Create a call back that records the evaluation history into eval_result.

Parameters: eval_result (dict) – A dictionary to store the evaluation results. callback – The requested callback function. function
xgboost.callback.reset_learning_rate(learning_rates)

Reset learning rate after iteration 1

NOTE: the initial learning rate will still take in-effect on first iteration.

Parameters: learning_rates (list or function) – List of learning rate for each boosting round or a customized function that calculates eta in terms of current number of round and the total number of boosting round (e.g. yields learning rate decay) list l: eta = l[boosting_round] function f: eta = f(boosting_round, num_boost_round) callback – The requested callback function. function
xgboost.callback.early_stop(stopping_rounds, maximize=False, verbose=True)

Create a callback that activates early stoppping.

Validation error needs to decrease at least every stopping_rounds round(s) to continue training. Requires at least one item in evals. If there’s more than one, will use the last. Returns the model from the last iteration (not the best one). If early stopping occurs, the model will have three additional fields: bst.best_score, bst.best_iteration and bst.best_ntree_limit. (Use bst.best_ntree_limit to get the correct value if num_parallel_tree and/or num_class appears in the parameters)

Parameters: stopp_rounds (int) – The stopping rounds before the trend occur. maximize (bool) – Whether to maximize evaluation metric. verbose (optional, bool) – Whether to print message about early stopping information. callback – The requested callback function. function