写出grid search的伪代码
时间: 2023-08-29 17:12:12 浏览: 54
1. 定义参数范围
2. 定义评估指标
3. 初始化最优参数和最优评估指标
4. 循环遍历所有参数组合:
- 设置当前参数组合
- 训练模型并进行评估
- 如果当前评估指标优于最优评估指标,则更新最优参数和最优评估指标
5. 返回最优参数和最优评估指标
伪代码如下:
```
parameters = {'param1': [value1, value2, ...], 'param2': [value1, value2, ...], ...}
evaluation_metric = 'accuracy'
best_params = {}
best_evaluation = 0
for p1 in parameters['param1']:
for p2 in parameters['param2']:
...
current_params = {'param1': p1, 'param2': p2, ...}
model = train_model(current_params)
evaluation = evaluate_model(model, evaluation_metric)
if evaluation > best_evaluation:
best_evaluation = evaluation
best_params = current_params
return best_params, best_evaluation
```