如何使用 GridSearchCV 测试回归交叉验证中的过度拟合？

Posted 2023-03-12

【中文标题】如何使用 GridSearchCV 测试回归交叉验证中的过度拟合？

【英文标题】：How to test for overfitting in regression cross-validation with GridSearchCV?

【发布时间】：2021-06-12 01:38:39

【问题描述】：

我正在运行一组连续变量和一个连续目标的回归模型。这是我的代码：

def run_RandomForest(xTrain,yTrain,xTest,yTest):
  cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1)

  # define the pipeline to evaluate
  model = RandomForestRegressor()
  fs = SelectKBest(score_func=mutual_info_regression)
  pipeline = Pipeline(steps=[('sel',fs), ('rf', model)])

  # define the grid
  grid = dict()
  grid['sel__k'] = [i for i in range(1, xTrain.shape[1]+1)]
  search = GridSearchCV(
        pipeline,
        param_grid=
            'rf__bootstrap': [True, False],
            'rf__max_depth': [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, None],
            'rf__max_features': ['auto', 'sqrt'],
            'rf__min_samples_leaf': [1, 2, 4],
            'rf__min_samples_split': [2, 5, 10],
            'rf__n_estimators': [200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800, 2000]
        ,
        scoring='neg_mean_squared_error',
        return_train_score=True,
        verbose=1,
        cv=5,
        n_jobs=-1)

  # perform the fitting
  results = search.fit(xTrain, yTrain)

  # predict prices of X_test
  y_pred = results.predict(xTest)

run_RandomForest(x_train,y_train,x_test_y_test)

我想了解这个模型是否过拟合。我读到合并交叉验证是检查这一点的有效方法。

您可以看到我已将 cv 合并到上面的代码中。但是，我完全坚持下一步。有人可以向我演示将获取 cv 信息的代码，并生成一个图表或一组统计数据，我打算分析这些数据是否过度拟合？我知道在 SO（例如 here 和 here）上有一些这样的问题，但我不明白如何具体地转化为我的情况，因为在这两个例子中，他们只是初始化一个模型和拟合它，而我的包含 GridSearchCV？

【参考方案1】：

您当然可以调整控制随机选择的特征数量的超参数，以从引导数据中生成每棵树。通常，您通过 k 折交叉验证来执行此操作；选择最小化测试样本预测误差的调整参数。此外，种植更大的森林将提高预测准确性，尽管一旦种植了数百棵树，回报通常会递减。

试试这个示例代码。

from sklearn.ensemble import RandomForestRegressor
rf = RandomForestRegressor(random_state = 42)
from pprint import pprint # Look at parameters used by our current forest

print(rf.get_params())

结果：

'bootstrap': True, 'ccp_alpha': 0.0, 'criterion': 'mse', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_impurity_split': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': None, 'verbose': 0, 'warm_start': False

还有……

import numpy as np
from sklearn.model_selection import RandomizedSearchCV # Number of trees in random forest
n_estimators = [int(x) for x in np.linspace(start = 200, stop = 2000, num = 10)]
# Number of features to consider at every split
max_features = ['auto', 'sqrt']
# Maximum number of levels in tree
max_depth = [int(x) for x in np.linspace(10, 110, num = 11)]
max_depth.append(None)
# Minimum number of samples required to split a node
min_samples_split = [2, 5, 10]
# Minimum number of samples required at each leaf node
min_samples_leaf = [1, 2, 4]
# Method of selecting samples for training each tree
bootstrap = [True, False]# Create the random grid
random_grid = 'n_estimators': n_estimators,
               'max_features': max_features,
               'max_depth': max_depth,
               'min_samples_split': min_samples_split,
               'min_samples_leaf': min_samples_leaf,
               'bootstrap': bootstrap
pprint(random_grid)

结果：

'bootstrap': [True, False],
 'max_depth': [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, None],
 'max_features': ['auto', 'sqrt'],
 'min_samples_leaf': [1, 2, 4],
 'min_samples_split': [2, 5, 10],
 'n_estimators': [200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800, 2000]

查看此链接了解更多信息。

https://towardsdatascience.com/optimizing-hyperparameters-in-random-forest-classification-ec7741f9d3f6

这里是一些做交叉验证的示例代码。

# import random search, random forest, iris data, and distributions
from sklearn.model_selection import cross_validate
from sklearn import datasets
from sklearn.ensemble import RandomForestClassifier

# get iris data
iris = datasets.load_iris()
X = iris.data
y = iris.target


model = RandomForestClassifier(random_state=1)
cv = cross_validate(model, X, y, cv=5)
print(cv)
print(cv['test_score'])
print(cv['test_score'].mean())

结果：

'fit_time': array([0.18350697, 0.14461398, 0.14261866, 0.13116884, 0.15478826]), 'score_time': array([0.01496148, 0.00997281, 0.00897574, 0.00797844, 0.01396227]), 'test_score': array([0.96666667, 0.96666667, 0.93333333, 0.96666667, 1.        ])
[0.96666667 0.96666667 0.93333333 0.96666667 1.        ]
0.9666666666666668

交叉验证的内部工作：

Shuffle the dataset in order to remove any kind of order
Split the data into K number of folds. K= 5 or 10 will work for most of the cases
Now keep one fold for testing and remaining all the folds for training
Train(fit) the model on train set and test(evaluate) it on test set and note down the results for that split
Now repeat this process for all the folds, every time choosing separate fold as test data
So for every iteration our model gets trained and tested on different sets of data
At the end sum up the scores from each split and get the mean score

【讨论】：

非常感谢

太棒了，我真的很感激