Notes on Parameter Tuning
Parameter tuning is a dark art in machine learning, the optimal parameters of a model can depend on many scenarios. So it is impossible to create a comprehensive guide for doing so.
This document tries to provide some guideline for parameters in XGBoost.
Understanding Bias-Variance Tradeoff
If you take a machine learning or statistics course, this is likely to be one of the most important concepts. When we allow the model to get more complicated (e.g. more depth), the model has better ability to fit the training data, resulting in a less biased model. However, such complicated model requires more data to fit.
Most of parameters in XGBoost are about bias variance tradeoff. The best model should trade the model complexity with its predictive power carefully. Parameters Documentation will tell you whether each parameter will make the model more conservative or not. This can be used to help you turn the knob between complicated model and simple model.
When you observe high training accuracy, but low test accuracy, it is likely that you encountered overfitting problem.
There are in general two ways that you can control overfitting in XGBoost:
The first way is to directly control model complexity.
The second way is to add randomness to make training robust to noise.
You can also reduce stepsize
eta. Remember to increase
num_roundwhen you do so.
Handle Imbalanced Dataset
For common cases such as ads clickthrough log, the dataset is extremely imbalanced. This can affect the training of XGBoost model, and there are two ways to improve it.
If you care only about the overall performance metric (AUC) of your prediction
Balance the positive and negative weights via
Use AUC for evaluation
If you care about predicting the right probability
In such a case, you cannot re-balance the dataset
max_delta_stepto a finite number (say 1) to help convergence
Reducing Memory Usage
If you are using a HPO library like
please control the number of threads it can use. It’s best to let XGBoost to run in
parallel instead of asking GridSearchCV to run multiple experiments at the same
time. For instance, creating a fold of data for cross validation can consume a significant
amount of memory:
# This creates a copy of dataset. X and X_train are both in memory at the same time. # This happens for every thread at the same time if you run `GridSearchCV` with # `n_jobs` larger than 1 X_train, X_test, y_train, y_test = train_test_split(X, y)
df = pd.DataFrame() # This creates a new copy of the dataframe, even if you specify the inplace parameter new_df = df.drop(...)
array = np.array(...) # This may or may not make a copy of the data, depending on the type of the data array.astype(np.float32)
# np by default uses double, do you actually need it? array = np.array(...)
You can find some more specific memory reduction practices scattered through the documents For instances: Distributed XGBoost with Dask, XGBoost GPU Support. However, before going into these, being conscious about making data copies is a good starting point. It usually consumes a lot more memory than people expect.