Hands-on Machine Learning with Scikit-Learn/

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Challenge Solution Review

In this lesson, we explain the solution to the last challenge lesson.

We'll cover the following...

Python 3.5

Saved

import sklearn.datasets as datasets
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.ensemble import GradientBoostingClassifier
X, y = datasets.load_breast_cancer(return_X_y=True)
train_x, test_x, train_y, test_y = train_test_split(X,
                                                    y,
                                                    test_size=0.2,
                                                    random_state=42)
gb = GradientBoostingClassifier(random_state=10)
param_grid = [{
    "n_estimators": [1, 2, 4, 16, 32],
    "learning_rate": [0.05, 0.1, 0.2, 0.4],
    "min_samples_leaf": [1, 2, 4, 8],
}]
cv = GridSearchCV(gb, param_grid=param_grid, scoring="f1", n_jobs=4)
cv.fit(train_x, train_y)
print("The best F1-score is {}.".format(cv.best_score_))
print("The parameter of best estimator is {}.".format(cv.best_params_))