使用时要缩小参数范围再使用网格搜索 gini_thresholds = np.linspace(0, 0.5, 20)#在0到0.5之间取出20个有序的随机数 parameters = {'splitter': ('best', 'random') , 'criterion': ("gini", "entropy") , "max_depth": [*range(1, 10)] , 'min_samples_leaf': [*range(1, 50, 5)]#1到5...
apply(X) Apply trees in the forest to X, return leaf indices. decision_path(X) Return the decision path in the forest fit(X, y[, sample_weight]) Build a forest of trees from the training set (X, y). get_params([deep]) Get parameters for this estimator. predict(X) Predict class ...
创建一个随机森林分类器实例，并设定随机种子以保证结果的可重复性。# 创建随机森林分类器实例rf=Random...
test_size=0.25,random_state=1234)#使用网格法找出最优越模型参数fromsklearn.model_selectionimportGridSearchCVfromsklearnimporttree#预设各参数的不同选项值max_depth=[2,3,4,5,6] min_samples_split=[2,4,6,8] min_samples_leaf=[2,4,8,10,12]#将各参数的值以字典的形式组织起来parameters={'max_d...
创建随机森林分类器实例：python rf = RandomForestClassifier()定义要调整的参数范围：python param_grid ...
1 设置环境 2 导入所需库和模块 3 加载数据集 4 数据集划分为训练集和测试集 5 数据预处理 6 参...
随机森林(Random Forest): 一种集成学习方法,通过构建多个决策树并进行投票来提高分类准确性。 能有效减少过拟合,提高模型的稳定性。 可应用于信用卡欺诈检测等任务。 梯度提升树(Gradient Boosting Trees, GBT): 另一种集成学习算法,通过逐步添加新的弱分类器来纠正前一个模型的错误。
Breiman, “Random Forests”, Machine Learning,45(1),5-32,2001. Methods predict(X) Predict class for X. The predicted class of an input sample is a vote by the trees in the forest, weighted by their probability estimates. That is, the predicted class is the one with highest mean probabi...
(test_Y,random_forest_predict)random_forest_RMSE=metrics.mean_squared_error(test_Y,random_forest_predict)**0.5print('Pearson correlation coefficient is {0}, and RMSE is {1}.'.format(random_forest_pearson_r[0],random_forest_RMSE))# Save key parametersexcel_file=load_workbook(write_excel_...