基于Python用支持向量机做回归预测,数据由本地导入,共有15000条数据,13个特征,采用optuna并结合网格搜索调参并可视化训练过程及R2随参数的变化曲线
时间: 2024-04-11 18:30:18 浏览: 28
您可以按照以下步骤基于Python使用支持向量机(SVM)进行回归预测,并结合Optuna和网格搜索进行参数调优,最后可视化训练过程及R2随参数的变化曲线。
1. 导入必要的库和模块:
```python
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.svm import SVR
import optuna
import matplotlib.pyplot as plt
```
2. 导入数据集:
```python
data = pd.read_csv('your_dataset.csv') # 替换为您的数据集路径
X = data.drop('target_column', axis=1) # 替换为您的自变量列名
y = data['target_column'] # 替换为您的因变量列名
```
3. 划分训练集和测试集:
```python
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
```
4. 定义SVM回归模型和目标函数:
```python
def objective(trial):
params = {
'C': trial.suggest_loguniform('C', 0.01, 100),
'epsilon': trial.suggest_uniform('epsilon', 0.01, 1),
'kernel': trial.suggest_categorical('kernel', ['linear', 'rbf']),
}
model = SVR(**params)
model.fit(X_train, y_train)
return model.score(X_test, y_test)
```
5. 使用Optuna进行参数搜索:
```python
study = optuna.create_study(direction='maximize')
study.optimize(objective, n_trials=100)
```
6. 获取最佳参数和模型:
```python
best_params = study.best_params
best_model = SVR(**best_params)
best_model.fit(X_train, y_train)
y_pred = best_model.predict(X_test)
r2_score = best_model.score(X_test, y_test)
```
7. 可视化训练过程及R2随参数的变化曲线:
```python
optuna.visualization.plot_optimization_history(study).show()
optuna.visualization.plot_slice(study).show()
optuna.visualization.plot_param_importances(study).show()
```
以上代码是一个基本的示例,您需要根据您的数据集和需求进行适当的修改。希望能对您有所帮助!