问内核密度得分与score_samples python scikitEN线性回归是简单易用的机器学习算法，scikit-learn是python...
estimators_samples:每个子树抽取的样本的子集 max_samples_:样本的真正数量 offset_:float offset用来从原始分数开始定义决策函数,其关系是decision_function=score_samples-offset_。假设behaviour == ‘new’,则offset_如下定义: 当contamination参数设置为'auto',当inliers的得分接近0且outliers的得分接近-1时,偏移量等...
在Scikit-Learn中,我们可以使用函数sklearn.metrics.calinski_harabasz_score()来计算这个值。 from sklearn.metrics import calinski_harabasz_score print(f'CH(2 clusters): {calinski_harabasz_score(X, labels_k2):.3f}') print(f'CH(3 clusters): {calinski_harabasz_score(X, labels_k3):.3f}') prin...
Score: -0.00000 Feature: 3, Score: -0.00000 Feature: 4, Score: 93.32225 Feature: 5, Score: 86.50811 Feature: 6, Score: 26.74607 Feature: 7, Score: 3.28535 Feature: 8, Score: -0.00000 Feature: 9, Score: 0.00000 然后为特征重要性得分创建条形图。这种...
Z = -clf.score_samples(XX) Z = Z.reshape(X.shape) CS = plt.contour(X, Y, Z, norm=LogNorm(vmin=1.0, vmax=1000.0), levels=np.logspace(0, 3, 10)) CB = plt.colorbar(CS, shrink=0.8, extend='both') plt.scatter(X_train[:, 0], X_train[:, 1], .8, edgecolors = [1, ...
x_log_prob = model.score_samples(x_range[:, np.newaxis]) # 这个东西返回概率的对数 x_prob = np.exp(x_log_prob) plt.figure(figsize=(10, 10)) r = plt.hist( x=x_train, bins=50, density=True, histtype='stepfilled', color='red', ...
base_score:基准分,默认为750。 card:自定义分箱结果,如果不提供,则会使用WOE转换进行自动分箱。 combiner:用于组合各个特征的得分,默认为空字典。 transer:用于转换原始特征到得分,默认为None。 **kwargs:其他可选参数,例如min_samples,用于控制分箱的最小样本数。 在调用toad.ScoreCard对象的fit方法时,需要传入...
y_under_pred).flatten()print((tn,fp,fn,tp))print("准确率：{:.4f}".format(accuracy_score(...
# do not use predict, decision_function and score_samples on X_train # as this would give wrong results but only on new unseen data(not # used in X_train , eg: X_test, X_outliers or the meshgrid) y_pred_test = clf.predict(X_test) ...
This example shows how to use LOF for novelty detection .Note that when LOF is used for novelty detection you MUST not use no predict, decision_function and score_samples on the training set as this would lead to wrong result. you must only use these methods on new unseen data(which are...