以下是一个简单的示例,使用Pyho和sklear库实现一个基于决策树的信用评分模型:
1. 导入必要的库和数据集
```pyhoimpor padas as pdfrom sklear.model_selecio impor rai_es_splifrom sklear.ree impor DecisioTreeClassifierfrom sklear.merics impor accuracy_score, cofusio_marix
# 加载数据集daa = pd.read_csv('credi_card.csv')```
2. 数据预处理和特征工程
```pyho# 缺失值处理daa.filla(0, iplace=True)
# 特征工程X = daa.drop('defaul', axis=1)y = daa['defaul']```
3. 划分训练集和测试集
```pyhoX_rai, X_es, y_rai, y_es = rai_es_spli(X, y, es_size=0.2, radom_sae=42)```
4. 训练模型并评估
```pyho# 创建决策树模型并训练clf = DecisioTreeClassifier()clf.fi(X_rai, y_rai)
# 在测试集上进行预测并评估模型性能y_pred = clf.predic(X_es)accuracy = accuracy_score(y_es, y_pred)pri('Accuracy:', accuracy)```
5. 可视化结果
可以使用maplolib等库将预测结果可视化。例如,可以绘制混淆矩阵:
```pyhoimpor maplolib.pyplo as plpl.figure(figsize=(10, 7))pl.imshow(cofusio_marix(y_es, y_pred), ierpolaio='eares', cmap=pl.cm.Blues)pl.ile('Cofusio marix')pl.colorbar()ick_labels = [0, 1]pl.xicks(ick_labels, ick_labels)pl.yicks(ick_labels, ick_labels)pl.ylabel('True label')pl.xlabel('Prediced label')pl.show()```