我尝试使用SciKit-Learn的网格搜索来查找随机森林的最佳参数。我按照以下方式执行此操作:
from sklearn.metrics import classification_report
from sklearn.pipeline import Pipeline
from sklearn.grid_search import GridSearchCV
pipeline = Pipeline([('clf', RandomForestRegressor(random_state=50))])
parameters = {
'clf__n_estimators': (50, 100, 200),
'clf__max_depth': (50, 150, 250),
'clf__min_samples_split': (1, 2, 3, 4, 5),
'clf__min_samples_leaf': (1, 2, 3, 4, 5)
}
grid_search = GridSearchCV(pipeline, parameters, n_jobs=-1,verbose=1, scoring='neg_mean_squared_error')
grid_search.fit(X, Y)
print 'Best score: %0.3f' % grid_search.best_score_
print 'Best parameters set:'
best_parameters = grid_search.best_estimator_.get_params()
for param_name in sorted(parameters.keys()):
print '\t%s: %r' % (param_name, best_parameters[param_name])
predictions = grid_search.predict(X)
print classification_report(Y, predictions)
不幸的是,我得到JobLibValueError
指向:
---> 14 grid_search.fit(X, Y)
作为参考,我的X看起来像这样:
0 1 2 3 4 5 6 7 8 9 ... 76613 76614 76615 76616 76617 76618 76619 76620 _engaged_time _title
0 0.0 0.000000 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 20000.0 54
1 0.0 0.000000 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 55000.0 40
我的Y值只是一堆参与时间(整数)。
感谢您的帮助!
答案 0 :(得分:0)
尝试
1)替换:
from sklearn.grid_search import GridSearchCV
<强>与强>
from sklearn.model_selection import GridSearchCV
2)更新sklearn模块
pip install -U scikit-learn
或conda install scikit-learn
解决方案1)解决了我遇到的类似问题。