importscikitplotasskplt 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。 importscikitplotasskplt rf = RandomForestClassifier() rf.fit(X, y) s...
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...
importscikitplotasskplt 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。 importscikitplotasskplt rf = RandomForestClassifier() rf.fit(X, y) skplt.estimators.plot_feature_importances( rf, f...
forest = RandomForestClassifier(n_estimators=100, random_state=0) # 构建一片森林,100棵树 forest.fit(X_train,y_train) # 输出拟合优度 print("Accuracy on training set: {:.3f}".format(forest.score(X_train, y_train))) # 训练集1.00 print("Accuracy on test set: {:.3f}".format(forest....
我们首先运行「随机森林分类模型」RandomForestClassifier (选 5 个决策树,n_jobs设置 -1 为了利用所有的内存)。 from sklearn.ensemble import RandomForestClassifier RF = RandomForestClassifier( n_estimators=5, random_state=seed, n_jobs=-1 )
(n_samples=1000, n_features=20, random_state=42) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 训练多个模型 models = { 'RandomForest': RandomForestClassifier(random_state=42), 'GradientBoosting': GradientBoostingClassifier(random_state=...
rf = RandomForestClassifier(max_depth = 4) idx = range(len(iris.target)) np.random.shuffle(idx) rf.fit(iris.data[idx][:100], iris.target[idx][:100]) 1. 2. 3. 4. 5. 6. 7. 8. 9. 对一个独立样本做预测。 instance = iris.data[idx][100:101] ...
import scikitplot as skpltrf = 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 skpltrf = RandomForestClassifier()rf.fit(X, y)skplt.estimators.plot_feature_importances(rf, fea...
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。 代码语言:javascript 代码运行次数:0 运行 AI代码解释 importscikitplotasskplt rf=RandomForestClassifier()rf.fit(X,y)skplt.estimators.plot_feature_importance...
rf = RandomForestClassifier() rf.fit(X, y) preds = rf.predict(X) skplt.metrics.plot_confusion_matrix(y_true=y, y_pred=preds) plt.show() ROC曲线 X, y = load_digits(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33) ...