from sklearn.metrics import average_precision_score y_true = np.array([0, 0, 1, 1]) y_score = np.array((0.1, 0.4, 0.35, 0.8)) prec, recall, threshold = precision_recall_curve(y_true, y_score) print(prec) print(recall) print(threshold) print(precision_recall_curve(y_true, y_sc...
sklearn.metrics.accuracy_score(y_true, y_pred, normalize=True, sample_weight=None) normalize:默认值为True,返回正确分类的比例;如果为False,返回正确分类的样本数 示例: >>>import numpy as np >>>from sklearn.metrics import accuracy_score >>>y_pred = [0, 2, 1, 3] >>>y_true = [0, 1...
建立两个逻辑回归,L1正则化和L2正则化的差别就一目了然了: fromsklearn.linear_modelimportLogisticRegressionasLRfromsklearn.datasetsimportload_breast_cancerimportnumpyasnpimportmatplotlib.pyplotaspltfromsklearn.model_selectionimporttrain_test_splitfromsklearn.metricsimportaccuracy_scoredata=load_breast_cancer(...
Thesklearn.metricsmodule implements functions assessing prediction error for specific purposes. These metrics are detailed in sections onClassification metrics,Multilabel ranking metrics,Regression metricsandClustering metrics. 分类模型 accuracy_score 分类准确率分数是指所有分类正确的百分比。分类准确率这一衡量分...
fromsklearn.linear_modelimportLogisticRegression as LR fromsklearn.datasetsimportload_breast_cancer importnumpy as np importmatplotlib.pyplot as plt fromsklearn.model_selectionimporttrain_test_split fromsklearn.metricsimportaccuracy_score data=load_breast_cancer() ...
accuracy_score函数计算了准确率,不管是正确预测的fraction(default),还是count(normalize=False)。 在multilabel分类中,该函数会返回子集的准确率。如果对于一个样本来说,必须严格匹配真实数据集中的label,整个集合的预测标签返回1.0;否则返回0.0. 预测值与真实值的准确率,在n个样本下的计算公式如下: 1(x)为指示函...
fromsklearn.linear_modelimportLogisticRegression as LRfromsklearn.datasetsimportload_breast_cancerimportnumpy as npimportmatplotlib.pyplot as pltfromsklearn.model_selectionimporttrain_test_splitfromsklearn.metricsimportaccuracy_score data=load_breast_cancer() ...
get_loc(i)) for i in x.columns] vif 代码语言:javascript 代码运行次数:0 运行 AI代码解释 x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=2021) clf=LogisticRegression(max_iter=300) clf.fit(x_train,y_train) y_pred=clf.predict(x_test) accuracy_score(...
accuracy_score函数计算了准确率,不管是正确预测的fraction(default),还是count(normalize=False)。 在multilabel分类中,该函数会返回子集的准确率。如果对于一个样本来说,必须严格匹配真实数据集中的label,整个集合的预测标签返回1.0;否则返回0.0. 预测值与真实值的准确率,在n个样本下的计算公式如下: ...
问在逻辑回归中使用sklearn中的accuracy_score时出错EN算法原理 传送门：机器学习实战之Logistic回归 正则...