前言
scikit-learn (sklearn)是Python环境下常见的机器学习库，包含了常见的分类、回归和聚类算法。在训练模型之后，常见的操作是对模型进行可视化，则需要使用Matplotlib进行展示。
scikit-plot是一个基于sklearn和Matplotlib的库，主要的功能是对训练好的模型进行可视化，功能比较简单易懂。
pip install scikit-plot
正文
功能1：评估指标可视化
scikitplot.metrics.plot_confusion_matrix快速展示模型预测结果和标签计算得到的混淆矩阵。
import scikitplot as skplt rf = RandomForestClassifier() rf = rf.fit(X_train, y_train) y_pred = rf.predict(X_test) skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=True) plt.show()
scikitplot.metrics.plot_roc快速展示模型预测的每个类别的ROC曲线。
import scikitplot as skplt nb = GaussianNB() nb = nb.fit(X_train, y_train) y_probas = nb.predict_proba(X_test) skplt.metrics.plot_roc(y_test, y_probas) plt.show()
scikitplot.metrics.plot_ks_statistic从标签和分数/概率生成 KS 统计图。
import scikitplot as skplt lr = LogisticRegression() lr = lr.fit(X_train, y_train) y_probas = lr.predict_proba(X_test) skplt.metrics.plot_ks_statistic(y_test, y_probas) plt.show()
scikitplot.metrics.plot_precision_recall从标签和概率生成PR曲线
import scikitplot as skplt nb = GaussianNB() nb.fit(X_train, y_train) y_probas = nb.predict_proba(X_test) skplt.metrics.plot_precision_recall(y_test, y_probas) plt.show()
scikitplot.metrics.plot_silhouette对聚类结果进行silhouette analysis分析
import scikitplot as skplt kmeans = KMeans(n_clusters=4, random_state=1) cluster_labels = kmeans.fit_predict(X) skplt.metrics.plot_silhouette(X, cluster_labels) plt.show()
scikitplot.metrics.plot_calibration_curve绘制分类器的矫正曲线
import scikitplot as skplt rf = RandomForestClassifier() lr = LogisticRegression() nb = GaussianNB() svm = LinearSVC() rf_probas = rf.fit(X_train, y_train).predict_proba(X_test) lr_probas = lr.fit(X_train, y_train).predict_proba(X_test) nb_probas = nb.fit(X_train, y_train).predict_proba(X_test) svm_scores = svm.fit(X_train, y_train).decision_function(X_test) probas_list = [rf_probas, lr_probas, nb_probas, svm_scores] clf_names = ['Random Forest', 'Logistic Regression', 'Gaussian Naive Bayes', 'Support Vector Machine'] skplt.metrics.plot_calibration_curve(y_test, probas_list, clf_names) plt.show()
功能2：模型可视化
scikitplot.estimators.plot_learning_curve生成不同训练样本下的训练和测试学习曲线图。
import scikitplot as skplt rf = RandomForestClassifier() skplt.estimators.plot_learning_curve(rf, X, y) plt.show()
scikitplot.estimators.plot_feature_importances可视化特征重要性。
import scikitplot as skplt rf = RandomForestClassifier() rf.fit(X, y) skplt.estimators.plot_feature_importances( rf, feature_names=['petal length', 'petal width', 'sepal length', 'sepal width']) plt.show()
功能3：聚类可视化
scikitplot.cluster.plot_elbow_curve展示聚类的肘步图。
import scikitplot as skplt kmeans = KMeans(random_state=1) skplt.cluster.plot_elbow_curve(kmeans, cluster_ranges=range(1, 30)) plt.show()
功能4：降维可视化
scikitplot.decomposition.plot_pca_component_variance绘制 PCA 分量的解释方差比。
import scikitplot as skplt pca = PCA(random_state=1) pca.fit(X) skplt.decomposition.plot_pca_component_variance(pca) >plt.show()
scikitplot.decomposition.plot_pca_2d_projection绘制PCA降维之后的散点图。
import scikitplot as skplt pca = PCA(random_state=1) pca.fit(X) skplt.decomposition.plot_pca_2d_projection(pca, X, y) plt.show()
