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 GridSearchCV(estimator,param_grid,scoring=None,fit_params=None,n_jobs=1,iid=True,refit=True,cv=None,verbose=0,pre_dispatch='2*n_jobs',error_score='raise',return_train_score=True) Parameters: estimator:所使用的分类器,或者pipeline param_grid:值为字典或者列表,即需要最优化的...
class sklearn.model_selection.GridSearchCV(estimator, param_grid, scoring=None, fit_params=None, n_jobs=1, iid=True, refit=True, cv=None, verbose=0, pre_dispatch=‘2*n_jobs’, error_score=’raise’, return_train_score=’warn’) (1)estimator 选择使用的分类器,并且传入除需要确定最佳的参...
class sklearn.model_selection.GridSearchCV(estimator, param_grid, scoring=None, fit_params=None, n_jobs=1, iid=True, refit=True, cv=None, verbose=0, pre_dispatch=‘2*n_jobs’, error_score=’raise’, return_train_score=’warn’) (1)estimator 选择使用的分类器,并且传入除需要确定最佳的参...
classsklearn.model_selection.GridSearchCV(estimator, param_grid, scoring=None, fit_params=None, n_jobs=1, iid=True, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', error_score='raise', return_train_score=True) GridSearchCV官方说明 ...
fit(X,y=None,groups=None,fit_params):在数据集上运行所有的参数组合 get_params(deep=True):返回估计器的参数 inverse_transform(Xt):Call inverse_transform on the estimator with the best found params. predict(X):返回预测结果值(0/1) predict_log_proba(X): Call predict_log_proba on the estimato...
# 导入模块from sklearn.model_selection importGridSearchCV# 函数全名classsklearn.model_selection.GridSearchCV(estimator,param_grid,scoring=None,fit_params=None,n_jobs=1,iid=True,refit=True,cv=None,verbose=0,pre_dispatch='2*n_jobs',error_score='raise',return_train_score=True)# 举例grid_search...
classsklearn.model_selection.GridSearchCV(estimator,param_grid,scoring=None,fit_params=None,n_jobs=1,iid=True,refit=True,cv=None,verbose=0,pre_dispatch=‘2*n_jobs’,error_score=’raise’,return_train_score=’warn’) estimator:scikit-learn 库里的算法模型; ...
如果不对数据进行拟合，就无法得到最佳参数。拟合数据
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_...