使用CatBoost和SHAP进行多分类完整代码示例

**使用CatBoost和SHAP进行多分类完整代码示例**

在本文中，我们将展示如何使用CatBoost进行多分类任务，并结合SHAP（SHapley Additive exPlanations）进行特征解释。

###依赖库首先，我们需要安装以下依赖库：

import pandas as pdfrom sklearn.model_selection import train_test_splitfrom catboost import CatBoostClassifier, Poolfrom shap import TreeExplainer

#读取数据df = pd.read_csv('data.csv')

# 切分数据集X = df.drop(['target'], axis=1)
y = df['target']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 创建CatBoost分类器model = CatBoostClassifier(
 iterations=100,
 learning_rate=0.1,
 depth=6,
 loss_function='MultiClass',
 eval_metric='MultiClass'
)

# 训练模型model.fit(X_train, y_train)

# 创建TreeExplainerexplainer = TreeExplainer(model)

# 得到SHAP值shap_values = explainer.shap_values(Pool(X_test))

# 打印SHAP值print(shap_values)

# 计算特征重要性feature_importance = pd.DataFrame({'feature': X_test.columns, 'importance': explainer.shap_values(Pool(X_test)).mean(axis=0)})

# 打印特征重要性print(feature_importance)

# 预测结果y_pred = model.predict(X_test)

# 计算准确率accuracy = (y_pred == y_test).mean()

# 打印准确率print('Accuracy:', accuracy)

import pandas as pdfrom sklearn.model_selection import train_test_splitfrom catboost import CatBoostClassifier, Poolfrom shap import TreeExplainer#读取数据df = pd.read_csv('data.csv')

# 切分数据集X = df.drop(['target'], axis=1)
y = df['target']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 创建CatBoost分类器model = CatBoostClassifier(
 iterations=100,
 learning_rate=0.1,
 depth=6,
 loss_function='MultiClass',
 eval_metric='MultiClass'
)

# 训练模型model.fit(X_train, y_train)

# 创建TreeExplainerexplainer = TreeExplainer(model)

# 得到SHAP值shap_values = explainer.shap_values(Pool(X_test))

# 打印SHAP值print(shap_values)

# 计算特征重要性feature_importance = pd.DataFrame({'feature': X_test.columns, 'importance': explainer.shap_values(Pool(X_test)).mean(axis=0)})

# 打印特征重要性print(feature_importance)

# 预测结果y_pred = model.predict(X_test)

# 计算准确率accuracy = (y_pred == y_test).mean()

# 打印准确率print('Accuracy:', accuracy)