以红酒数据集和波士顿房价数据集为例,sklearn中的分类树和回归树的简单使用如下: # 导包 from sklearn.datasets import load_wine, load_boston from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor # 分类树 data_wine = load_wine()...
from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) 3、模型训练 使用RandomForestRegressor训练模型。 from sklearn.ensemble import RandomForestRegressor rf = RandomForestRegressor(n_estimators=100, ran...
from sklearn.datasets import fetch_california_housing from sklearn.ensemble import RandomForestRegressor from sklearn.metrics import mean_squared_error class HousingPricePredictor: def __init__(self, n_estimators=100, max_depth=None, random_state=42): """初始化随机森林回归模型""" self.n_estimat...
以红酒数据集和波士顿房价数据集为例,sklearn中的分类树和回归树的简单使用如下: # 导包fromsklearn.datasetsimportload_wine, load_bostonfromsklearn.model_selectionimporttrain_test_splitfromsklearn.ensembleimportRandomForestClassifier, RandomForestRegressor# 分类树data_wine = load_wine()# 加载红酒数据集# ...
本文简要介绍python语言中sklearn.ensemble.RandomForestRegressor的用法。 用法: classsklearn.ensemble.RandomForestRegressor(n_estimators=100, *, criterion='squared_error', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='auto', max_leaf_nodes=No...
在调参之前,我们先建立一个初始的Random Forest回归模型: fromsklearn.ensembleimportRandomForestRegressorfromsklearn.model_selectionimporttrain_test_split# 拆分样本数据X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=42)# 初始化随机森林回归模型model=RandomForestRegressor(...
from sklearn.ensemble import RandomForestRegressor # 训练随机森林模型,为1000棵树 rf_most_important = RandomForestRegressor(n_estimators=1000,random_state=42) # 拿到 temp_1 与 average 特征 important_train_features = train_features.loc[:,["temp_1","average"]] ...
python库之sklearn 一、安装sklearn conda install scikit-learn 参考文献 [1]整体介绍sklearn https://blog.csdn.net/u014248127/article/details/78885180 二、介绍RandomForestRegressor 1sklearn.ensemble.RandomForestRegressor( n_estimators=10,2criterion='mse',3max_depth=None,4min_samples_split=2,5min_...
在Python中,我们使用sklearn库的RandomForestRegressor类来构建随机森林回归器。它具有以下参数: 1. n_estimators:指定用于构建随机森林的决策树数量,默认值为100。 2. criterion:指定用于衡量决策树分裂质量的评价准则,可以是“mse”(均方误差)或“mae”(平均绝对误差),默认值为“mse”。 3. max_depth:指定决策树...
python3 学习机器学习api 使用了三种集成回归模型 git: https://github.com/linyi0604/MachineLearning 代码: 1fromsklearn.datasetsimportload_boston2fromsklearn.cross_validationimporttrain_test_split3fromsklearn.preprocessingimportStandardScaler4fromsklearn.ensembleimportRandomForestRegressor, ExtraTreesRegressor, Gr...