在二进制分类中,此函数等于jaccard_score 函数。 例子: >>> from sklearn.metrics import accuracy_score >>> y_pred = [0, 2, 1, 3] >>> y_true = [0, 1, 2, 3] >>> accuracy_score(y_true, y_pred) 0.5 >>> accuracy_score(y_true, y_pred, normalize=False) 2 在具有二进制标签指...
print(accuracy_score(y_true, y_pred)) # 0.5 print(accuracy_score(y_true, y_pred, normalize=False)) # 2 # 在具有二元标签指示符的多标签分类案例中 print(accuracy_score(np.array([[0, 1], [1, 1]]), np.ones((2, 2))) # 0.5 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 2....
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 >>>importnumpyasnp >>>fromsklearn.metricsimportaccuracy_score >>>y_pred=[0,2,1,3] >>>y_true=[0,1,2,3] >>>accuracy_score(y_...
我们使用一个名为“knn_classifier”的机器学习模型,并使用一个名为“accuracy_score”的函数来计算模型的准确率。 fromsklearn.datasetsimportload_irisfromsklearn.model_selectionimporttrain_test_splitfromsklearn.neighborsimportKNeighborsClassifierfromsklearn.metricsimportaccuracy_score# 加载数据集iris=load_iris()...
accuracy_score 精确性,模型评估指标之一 详细参数 class sklearn.linear_model.LogisticRegression (penalty=’l2’, dual=False, tol=0.0001, C=1.0,fit_intercept=True, intercept_scaling=1, class_weight=None, random_state=None, solver=’warn’, max_iter=100,multi_class=’warn’, verbose=0, warm_...
from sklearn.metrics import accuracy_score yp = [1, 0, 1, 1] y = [1, 0, 0, 1] print("【显示】yp =",yp) print("【显示】y =",y) print("【执行】accuracy_score(yp, y)") print(accuracy_score(yp, y)) print("【执行】accuracy_score(y, yp)") ...
1.分类任务:对于分类任务,score函数通常会返回准确率(accuracy)。准确率是指模型正确预测的样本数与总样本数的比值。例如,如果模型在100个样本中正确预测了80个样本的分类,那么准确率为80%。 2.回归任务:对于回归任务,score函数通常会返回决定系数(coefficient of determination)。决定系数是用于度量回归模型的好坏的指...
accuracy = accuracy_score(y_test, y_pred) print("准确率:", accuracy) ``` # 预测 X_test = vectorizer.transform(newsgroups_test.data) y_pred = mnb.predict(X_test) ``` sklearn提供了三种常用的贝叶斯算法实现,分别是高斯朴素贝叶斯、多项式朴素贝叶斯和伯努利朴素贝叶斯。根据不同的数据特征和应用场...
accuracy = clf.score(X, y) print("准确率:", accuracy) ``` 3.对于回归问题,使用线性回归模型进行预测,并计算R2分数: ```python from sklearn.linear_model import LinearRegression from sklearn.datasets import make_regression X, y = make_regression(n_samples=100, n_features=1, noise=0.1) reg...
sklearn.metrics中accuracy_score函数用来判断模型预测的准确度。 ### 性能度量 from sklearn.metrics import accuracy_score # 准确率 accuracy = accuracy_score(y_test,y_pred) print("accuarcy: %.2f%%" % (accuracy*100.0)) 5.特征重要性 xgboost分析了特征的重要程度,通过函数plot_importance绘制图片。