random_state:用来设置分枝中的随机模式的参数,默认为None; bootarap:代表采用有放回的随机抽样技术; obb_score:袋外数据测试,将obb_score参数调整为True,训练完毕后,用obb_score_属性来查看袋外数据上测试的结果; 随机森林四个常用接口:apply;fit;predict;score 2. 随机森林回归 RandomForestRegressor 与分类基本差...
1、导入数据 import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn...
estimators = [("Tree", DecisionTreeRegressor()), ("RandomForestRegressor", RandomForestRegressor(random_state=100)), ("ExtraTreesClassifier", ExtraTreesRegressor(random_state=100)), ] n_estimators = len(estimators) # Generate data def f(x): x = x.ravel() return np.exp(-x ** 2) + ...
FontTian 0 482 随机森林 2019-08-21 13:44 −from sklearn.ensemble import RandomForestRegressor #导入随机森林的包 import pandas as pd #加载入数据,这里用的是住房的数据 from sklearn.datasets.california_housing impor... admin9s 0 203 <123>...
@raghavrvI'm not so sure about these methods having a "_best_estimator" which is used. This kind of makes sense when you can have an ensemble of estimators with different hyperparameters, but with trees the idea is that since you're building them iteratively there is only one forest at ...
1 提升集成算法:重要参数n_estimators 1. 导入需要的库,模块以及数据 from xgboost import XGBRegressor as XGBR from sklearn.ensemble import RandomForestRegressor as RFR from skl
机器学习sklearn(88):算法实例(45)分类(24)XGBoost(二)梯度提升树(一)重要参数n_estimators,1提升集成算法:重要参数n_estimators1.导入需要的库,模块以及数据fromxgboostimportXGBRegressorasXGBRfromsklearn.ensembleimportRandomForestRegressorasRFRfromsk