How to optimise size depth of trees in XGBoost?

How to optimise size depth of trees in XGBoost?

How to optimise size depth of trees in XGBoost?

This recipe helps you optimise size (depth) of trees in XGBoost


Recipe Objective

Many a times while working on a dataset and using a Machine Learning model we don"t know which set of hyperparameters will give us the best result. Passing all sets of hyperparameters manually through the model and checking the result might be a hectic work and may not be possible to do.

To get the best set of hyperparameters we can use Grid Search. Grid Search passes all combinations of hyperparameters one by one into the model and check the result. Finally it gives us the set of hyperparemeters which gives the best result after passing in the model.

So this recipe is a short example of how can optimise size depth of trees in XGBoost.

Step 1 - Import the library - GridSearchCv

from sklearn import datasets from sklearn.model_selection import train_test_split from xgboost import XGBClassifier from sklearn.model_selection import GridSearchCV from sklearn.model_selection import StratifiedKFold import matplotlib matplotlib.use("Agg") from matplotlib import pyplot

Here we have imported various modules like StratifiedKFold, datasets, XGBClassifier and GridSearchCV from differnt libraries. We will understand the use of these later while using it in the in the code snipet.
For now just have a look on these imports.

Step 2 - Setup the Data

Here we have used datasets to load the inbuilt wine dataset and we have created objects X and y to store the data and the target value respectively. We have used test_train_split to split the dataset. dataset = datasets.load_wine() X =; y = X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)

Step 3 - Using GridSearchCV

We have used XGBClassifier as a model. We have created a dictionary param_grid with parameters which we wabt to optimise. Finally we have used GridSearchCV to train and fit. Before using GridSearchCV, lets have a look on the important parameters.

  • estimator: In this we have to pass the models or functions on which we want to use GridSearchCV
  • param_grid: Dictionary or list of parameters of models or function in which GridSearchCV have to select the best.
  • Scoring: It is used as a evaluating metric for the model performance to decide the best hyperparameters, if not especified then it uses estimator score.
model = XGBClassifier() max_depth = range(1, 11, 2) print(max_depth) param_grid = dict(max_depth=max_depth) kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=7) grid_search = GridSearchCV(model, param_grid, scoring="neg_log_loss", n_jobs=-1, cv=kfold, verbose=1) grid_result =, y)

Step 4 - Printing Results

Now we are using print statements to print the results. It will give the values of hyperparameters as a result. print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_)) means = grid_result.cv_results_["mean_test_score"] stds = grid_result.cv_results_["std_test_score"] params = grid_result.cv_results_["params"] for mean, stdev, param in zip(means, stds, params): print("%f (%f) with: %r" % (mean, stdev, param)) As an output we get:

range(1, 11, 2)
Fitting 10 folds for each of 5 candidates, totalling 50 fits
[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.
[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:   29.2s

Best: -0.069259 using {"max_depth": 1}

-0.069259 (0.034427) with: {"max_depth": 1}
-0.083225 (0.059937) with: {"max_depth": 3}
-0.086606 (0.061344) with: {"max_depth": 5}
-0.086606 (0.061344) with: {"max_depth": 7}
-0.086606 (0.061344) with: {"max_depth": 9}
[Parallel(n_jobs=-1)]: Done  50 out of  50 | elapsed:   29.4s finished

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