回归模型的评判标准
from sklearn import datasets from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression import numpy as np boston = datasets.load_boston() X =boston.data[:,:1] y = boston.target X_train,X_test,y_train,y_test = train_test_split(X,y) line_clf = LinearRegression() line_clf.fit(X_train,y_train) y_predict = line_clf.predict(X_test) MSE
from sklearn.metrics import mean_squared_error mean_squared_error(y_test,y_predict) 71.122 RMSE
from sklearn.metrics import mean_squared_error np.sqrt(mean_squared_error(y_test,y_predict)) 8.8337 MAE
from sklearn.metrics import mean_absolute_error mean_absolute_error(y_test,y_predict) 5.3726 R方
我们一般用R方来作为回归模型的准确率
from sklearn.metrics import r2_score r2_score(y_test,y_predict) 0.66239 line_clf.score(X_test,y_test) 0.66239 分类模型的评判标准
import numpy as np from sklearn import datasets import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split breast_cancer = datasets.load_breast_cancer() X = breast_cancer.data y = breast_cancer.target X_train,X_test,y_train,y_test = train_test_split(X,y) from sklearn.linear_model import LogisticRegression from sklearn.preprocessing import PolynomialFeatures poly = PolynomialFeatures(degree=2) log_reg = LogisticRegression(C=3) poly.fit(X_train) X_train = poly.transform(X_train) X_test =poly.transform(X_test) log_reg.fit(X_train,y_train) y_predict= log_reg.predict(X_test) 精准率
from sklearn.metrics import precision_score precision_score(y_test,y_predict) 0.53191 召回率
from sklearn.metrics import recall_score recall_score(y_test,y_predict) 0.53191 F1(精准率与召回率的平衡)
from sklearn.metrics import f1_score f1_score(y_test,y_predict) 0.53191 ROC曲线
from sklearn.metrics import roc_curve decision_scores = log_reg.decision_function(X_test) fprs,tprs,thresholds = roc_curve(y_test,decision_scores) plt.plot(fprs,tprs) plt.show() 
from sklearn.metrics import roc_auc_score roc_auc_score(y_test, decision_scores) # 我们可以使用这个来判定被曲线包围的面积有多少 0.26574 版权声明：本文内容由互联网用户自发贡献，该文观点仅代表作者本人。本站仅提供信息存储空间服务，不拥有所有权，不承担相关法律责任。如发现本站有涉嫌侵权/违法违规的内容， 请联系我们举报，一经查实，本站将立刻删除。
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