一、数据预处理 1. 数据拆分 sklearn.model_selection.train_test_split() 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 from sklearn.datasets import load_wine from sklearn.model_selection import train_test_split import pandas as pd wine = load_wine() # 字典 feats = wine["data"] feats_name = wine["feature_names"] feats_df = pd.DataFrame(feats, columns=feats_name) targets...
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 import pandas as pd from sklearn.datasets import load_wine from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier tree_classif = DecisionTreeClassifier() wine = load_wine() # 字典 feats = wine["data"] feats_name = wine["feature_names"] feats_df = pd.DataFrame(feats, columns=feats_name) targets = wine["target"].reshape((-1,1)) #feats_df.shape, targets.shape train_X, test_X, train_y, test_y = train_test_split(feats_df, targets, test_size=0.2, random_state=42) train_X.shape,...
目的：演示常见几种分类器的使用方法，对其超参数调优候选超参数的选择 0、示例数据 1 2 3 4 5 6 7 8 9 10 11 12 from sklearn import datasets from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.model_selection import cross_val_score from sklearn.model_selection import...
目的：演示常见几种回归器的使用方法，对其超参数调优候选超参数的选择 0、示例数据 1 2 3 4 5 6 7 8 9 10 11 12 from sklearn import datasets from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.model_selection import cross_val_score from sklearn.model_selection import...