首页X=pd.concat([card['Contacts_Count_12_mon'],card['Months_Inactive_12_mon'],card['Total_Amt_Chng_Q4_Q1'],card['Total_Relationship_Count'],card['Total_Trans_Amt'],card['Avg_Utilization_Ratio'],card['Total_Revolving_Bal'],card['Total_Trans_Ct'],card['Gender'],Education_Level_onehot,Income_Category_onehot,Marital_Status_onehot],axis=1) y=card['Attrition_Flag'] from sklearn.tree import DecisionTreeClassifier clf = DecisionTreeClassifier() clf.fit(X, y) y_pred = clf.predict(X) y = y.values.reshape(-1, 1) y_pred = y_pred.reshape(-1, 1) plt.scatter(y, y_pred, color='red', label='Predicted Values') plt.scatter(y, clf.predict(y), color='blue', label='True Values') plt.xlabel('True Values') plt.ylabel('Predictions') plt.title('Decision Tree Regression') plt.legend() plt.show() plt.show()报错X has 1 features, but DecisionTreeClassifier is expecting 23 features as input.

X=pd.concat([card['Contacts_Count_12_mon'],card['Months_Inactive_12_mon'],card['Total_Amt_Chng_Q4_Q1'],card['Total_Relationship_Count'],card['Total_Trans_Amt'],card['Avg_Utilization_Ratio'],card['Total_Revolving_Bal'],card['Total_Trans_Ct'],card['Gender'],Education_Level_onehot,Income_Category_onehot,Marital_Status_onehot],axis=1) y=card['Attrition_Flag'] from sklearn.tree import DecisionTreeClassifier clf = DecisionTreeClassifier() clf.fit(X, y) y_pred = clf.predict(X) y = y.values.reshape(-1, 1) y_pred = y_pred.reshape(-1, 1) plt.scatter(y, y_pred, color='red', label='Predicted Values') plt.scatter(y, clf.predict(y), color='blue', label='True Values') plt.xlabel('True Values') plt.ylabel('Predictions') plt.title('Decision Tree Regression') plt.legend() plt.show() plt.show()报错X has 1 features, but DecisionTreeClassifier is expecting 23 features as input.

时间: 2023-12-29 13:06:22 浏览: 122

根据你提供的代码，你尝试使用 DecisionTreeClassifier 模型对一个包含多个特征的数据集进行分类。然而，从代码中可以看出，你尝试将所有特征都合并到了 X 变量中，但是数据集中可能存在一些缺失值或者其他问题导致特征数量不一致。建议你先检查数据集中特征的数量和命名是否与代码中一致，确保数据集的正确性。同时，你还需要对数据集进行必要的预处理和特征工程，以便更好地训练模型。

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