# 需要导入模块: from sklearn.ensemble import RandomForestClassifier [as 别名]# 或者: from sklearn.ensemble.RandomForestClassifier importapply[as 别名]defmodelselect(input_filename, num_test_examples, block_size, n_estimators=100):# Perform some model selection to determine good parameters# Load d...
#Create a random forest classifier with the following Parameters trees = 250 max_feat = 7 max_depth = 30 min_sample = 2 clf = RandomForestClassifier(n_estimators=trees, max_features=max_feat, max_depth=max_depth, min_samples_split= min_sample, random_state=0, n_jobs=-1) #Train the ...
y, X_test, y_test):X, X1, y, y1 = train_test_split(X, y, test_size=0.5)#clf1 = GradientBoostingClassifier(n_estimators=10)#clf1 = RandomForestClassifier(n_estimators=20)clf1 = ExtraTreesClassifier(n_estimators=
Python is by far the most popular language in science, due in no small part to the ease at which it can be used and the vibrant ecosystem of user-generated packages. To install packages, there are two main methods: Pip (invoked as pip install), the package manager that comes bundled wi...
Python is by far the most popular language in science, due in no small part to the ease at which it can be used and the vibrant ecosystem of user-generated packages. To install packages, there are two main methods: Pip (invoked as pip install), the package manager that comes bundled ...
Stream Link Subset Multidimensional Raster Summarize Categorical Raster Summarize Raster Within Surface Parameters Tabulate Area Train Classifier Train Deep Learning Model Train Random Trees Regression Model Uninstall Deep Learning Model Watershed Zonal Geometry As Table Zonal Statistics Zonal Statistics As Table...
Python is by far the most popular language in science, due in no small part to the ease at which it can be used and the vibrant ecosystem of user-generated packages. To install packages, there are two main methods: Pip (invoked as pip install), the package manager that comes bundled ...
from sklearn.ensemble import RandomForestClassifier import numpy as np #fitting/examining tree s2=DecisionTreeClassifier(max_depth=3, random_state=5) s2.fit(x,cuisine) leaves2=pd.Series(s2.apply(x),name='leaves') idk=pd.concat([cuisine,leaves2],axis=1) ...
在下文中一共展示了GradientBoostingClassifier.apply方法的12个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。 示例1: transform_with_gbm_to_categorical ▲点赞 7▼
在下文中一共展示了RandomForestRegressor.apply方法的4个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。 示例1: test_drf_regressor_backupsklearn ▲点赞 6▼ # 需要导入模块: from sklearn.ensemble import RandomForestRegressor...