我在评估我的决策树分类器，并尝试绘制特征重要性。图表能够正确打印出来，但它打印了所有（80多个）特征，这导致视觉效果非常混乱。我正在尝试找出如何仅绘制重要变量的图表，并按照重要性排序。

您可以下载数据集到工作目录的链接，文件名为（”File”）： https://github.com/Arsik36/Python

最小可复现代码：

   import pandas as pd        import matplotlib.pyplot as plt        import seaborn as sns        from sklearn.model_selection import train_test_split          from sklearn.tree import DecisionTreeClassifier        file = 'file.xlsx'        my_df = pd.read_excel(file)        # 确定响应变量        my_df_target = my_df.loc[ :, 'Outcome']        # 确定解释变量        my_df_data = my_df.drop('Outcome', axis = 1)        # 声明带有分层的train_test_split        X_train, X_test, y_train, y_test = train_test_split(my_df_data,                                                            my_df_target,                                                            test_size = 0.25,                                                            random_state = 331,                                                            stratify = my_df_target)    # 声明类权重    weight = {0: 455, 1:1831}    # 实例化决策树分类器    decision_tree = DecisionTreeClassifier(max_depth = 5,                                           min_samples_leaf = 25,                                           class_weight = weight,                                           random_state = 331)    # 拟合训练数据    decision_tree_fit = decision_tree.fit(X_train, y_train)    # 在测试数据上预测    decision_tree_pred = decision_tree_fit.predict(X_test)# 声明X_train数据中的特征数量n_features = X_train.shape[1]# 设置绘图窗口figsize = plt.subplots(figsize = (12, 9))# 指定绘图内容plt.barh(range(n_features), decision_tree_fit.feature_importances_, align = 'center')plt.yticks(pd.np.arange(n_features), X_train.columns)plt.xlabel("重要程度")plt.ylabel("特征")

我目前尝试限制的输出仅为重要特征：

回答：

您需要修改所有的绘图代码以移除低重要性特征，可以尝试以下代码（未经测试）：

# 设置绘图窗口figsize = plt.subplots(figsize = (12, 9))featues_mask = tree.feature_importances_> 0.005# 指定绘图内容plt.barh(range(sum(featues_mask)), tree.feature_importances_[featues_mask], align = 'center')plt.yticks(pd.np.arange(sum(featues_mask)), X_train.columns[featues_mask])plt.xlabel("重要程度")plt.ylabel("特征")