from sklearn.model_selection import cross_val_score from sklearn.datasets import make_blobs from sklearn.ensemble import RandomForestClassifier from sklearn.ensemble import ExtraTreesClassifier from sklearn.tree import DecisionTreeClassifier import pandas as pd import numpy as np import matplotlib.pyplot as plt import sklearn.preprocessing as preprocessing from sklearn import preprocessing from sklearn.linear_model import LinearRegression from sklearn.neighbors import KNeighborsClassifier from sklearn.model_selection import train_test_split from sklearn.preprocessing import PolynomialFeatures dataset_x,dataset_y=return_data.return_traindata() X_train, X_test, y_train, y_test = train_test_split(dataset_X, dataset_Y, test_size=0.2, random_state=42) alphas=[10,15,20,25,30] test_sores=[] for alpha in alphas: clf = ExtraTreesClassifier(n_estimators=alpha, max_depth=None, min_samples_split=3, random_state=1) sore=cross_val_score(clf,X_train, y_train.ravel(),cv=5, scoring='accuracy') #交叉验证 test_sores.append(np.mean(sore)) print("testing",str(alpha)) print(test_sores) plt.figure() plt.plot(alphas,test_sores, color='blue') plt.scatter(alphas,test_sores,s=75,c="red",alpha=0.5) plt.show()
sklearn交叉验证(acc)
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