我在 scikit-learn 的 20 个新闻组数据集上训练了一个二元随机森林分类器。我想调整参数并通过网格搜索和训练数据的 3 折交叉验证来尝试。

这种方法有什么问题吗？

对于max_depth参数，我得到一个非常高的 500 值，这似乎太高了。有什么建议吗？

代码是：

from __future__ import print_function  
import sklearn  
import sklearn.ensemble  
import sklearn.metrics  
from sklearn.datasets import fetch_20newsgroups  
from sklearn.grid_search import GridSearchCV

categories = ['sci.med', 'soc.religion.christian']  
newsgroups_train = fetch_20newsgroups(subset='train', categories=categories, 
remove=('headers', 'footers', 'quotes'))  
newsgroups_test = fetch_20newsgroups(subset='test', categories=categories, 
remove=('headers', 'footers', 'quotes'))  
class_names = ['medicine', 'christian']  

vectorizer = 
sklearn.feature_extraction.text.TfidfVectorizer(lowercase=False)  
train_vectors = vectorizer.fit_transform(newsgroups_train.data)  
test_vectors = vectorizer.transform(newsgroups_test.data)  

rf = sklearn.ensemble.RandomForestClassifier(max_features='sqrt')  

param_grid = {  
           "n_estimators" : [10, 100, 1000],  
           "max_depth" : [5, 100, 500],  
           "min_samples_leaf" : [1, 20, 40]}  

CV_rf = GridSearchCV(estimator=rf, param_grid=param_grid)  
CV_rf.fit(train_vectors, newsgroups_train.target)  
print(CV_rf.best_params_)