模型训练pipeline

基于数十种统计类型特征，构建LR回归模型。代码逻辑包含：样本切分、特征预处理、模型训练、模型评估、特征重要性的可视化。

步骤一：导入所需库

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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, PolynomialFeatures
from sklearn.metrics import mean_squared_error, r2_score

步骤二：读取数据

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data = pd.read_csv('data.csv')

步骤三：数据预处理

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# 去除缺失值
data.dropna(inplace=True)

# 划分自变量和因变量
X = data.iloc[:, :-1]
y = data.iloc[:, -1]

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)

# 构建pipeline
pipeline = Pipeline([
    ('scaler', StandardScaler()),
    ('poly', PolynomialFeatures(degree=2, include_bias=False)),
    ('reg', LinearRegression())
])

# 训练模型
pipeline.fit(X_train, y_train)

# 预测结果
y_pred = pipeline.predict(X_test)

步骤四：模型评估

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# 均方误差
mse = mean_squared_error(y_test, y_pred)

# R2值
r2 = r2_score(y_test, y_pred)

print('MSE: %.3f' % mse)
print('R2 score: %.3f' % r2)

步骤五：特征重要性的可视化

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# 获取特征重要性
importance = pipeline.named_steps['reg'].coef_

# 将特征重要性与对应特征名对应
feature_names = pipeline.named_steps['poly'].get_feature_names(X.columns)
feature_importance = pd.DataFrame({'Feature': feature_names, 'Importance': importance})
feature_importance = feature_importance.sort_values('Importance', ascending=False)

# 绘制水平条形图
plt.figure(figsize=(10, 8))
plt.barh(feature_importance['Feature'], feature_importance['Importance'])
plt.title('Feature importance')
plt.xlabel('Importance')
plt.ylabel('Feature')
plt.show()

【sklearn】回归模型常规建模流程

模型训练pipeline