机器学习模型可视化：基于sklearn和Matplotlib的库scikit-plot|聚类|svm|matplotlib

机器学习模型可视化：基于sklearn和Matplotlib的库scikit-plot

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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。

https://scikit-plot.readthedocs.io

pip install scikit-plot

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()

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