效果: gsearch.best_params_ {'max_depth': 4, 'n_estimators': 100} gsearch.best_score_ 0.868142228555714 1. 2. 3. 4.
如果不对数据进行拟合，就无法得到最佳参数。拟合数据
sklearn中提供了这样的库代替了我们手动去试的过程,就是GridSearchCV,他会自己组合不同参数的取值,然后输出效果最好的一组参数。...GridSearchCV参数解释 GridSearchCV(estimator, param_grid, scoring=None, fit_params=None, \ ...
grid_scores_:给出不同参数情况下的评价结果 best_params_:描述了已取得最佳结果的参数的组合 best_score_:成员提供优化过程期间观察到的最好的评分 属性方法: grid.fit( train_x, train_y ):运行网格搜索 grid_scores_:给出不同参数情况下的评价结果 best_params_:描述了已取得最佳结果的参数的组合 best_sco...
效果: gsearch.best_params_{'max_depth': 4, 'n_estimators': 100}gsearch.best_score_0.868142228555714
(grid.best_params_)grid_y_pred = grid.predict(X_test)# 打印报告print(classification_report(y_test, grid_y_pred))### 输出结果如下:###"""Fitting 5 folds for each of 8 candidates, totalling 40 fits[CV 2/5] END ...C=0.1, gamma=scale, kernel=linear; total time= 0.0s[CV 1/5]...
randomized_search.fit(x_train, y_train)# Printing the best parametersprint(randomized_search.best_params_)# Training the decision tree model with the best hyperparametersbest_dt_model = randomized_search.best_estimator_# Evaluating the model on the testing sety_pred = best_dt_model.predict(x_...
print(randomized_search.best_params_) # Training the decision tree model with the best hyperparameters best_dt_model = randomized_search.best_estimator_ # Evaluating the model on the testing set y_pred = best_dt_model.predict(x_test)
print("最佳参数:", grid_search.best_params_) print("最佳分数:", grid_search.best_score_) # 使用最佳参数创建新的SVM模型 best_svm_model = grid_search.best_estimator_ # 使用最佳模型进行预测 best_Y_pred = best_svm_model.predict(X_test) ...
("Best parameters set found on development set:") print(clf.best_params_) print("Grid scores on development set:") means = clf.cv_results_['mean_test_score'] stds = clf.cv_results_['std_test_score'] for mean, std, params in zip(means, stds, clf.cv_results_['params']): print...