scikit-plot可视化模型

pip install scikit-plot

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()

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()

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()

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()

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()

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()

import scikitplot as skplt
rf = RandomForestClassifier()

skplt.estimators.plot_learning_curve(rf, X, y)
plt.show()

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()

import scikitplot as skplt
kmeans = KMeans(random_state=1)

skplt.cluster.plot_elbow_curve(kmeans, cluster_ranges=range(1, 30))
plt.show()

import scikitplot as skplt
pca = PCA(random_state=1)
pca.fit(X)

skplt.decomposition.plot_pca_component_variance(pca)
>plt.show()

import scikitplot as skplt
pca = PCA(random_state=1)
pca.fit(X)

skplt.decomposition.plot_pca_2d_projection(pca, X, y)
plt.show()