GridSearchCV.best_score_的含义是评分设置为“准确性"和CV [英] GridSearchCV.best_score_ meaning when scoring set to 'accuracy' and CV
问题描述
我正在尝试在知名的威斯康星州癌症数据集(569个样本，31个特征+目标)上找到适用于乳腺癌样本分类的最佳模型神经网络模型.我正在使用sklearn 0.18.1.到目前为止，我还没有使用Normalization.解决此问题后，我会添加它.
I'm trying to find the best model Neural Network model applied for the classification of breast cancer samples on the well-known Wisconsin Cancer dataset (569 samples, 31 features + target). I'm using sklearn 0.18.1. I'm not using Normalization so far. I'll add it when I solve this question.
# some init code omitted
X_train, X_test, y_train, y_test = train_test_split(X, y)
为GridSearchCV定义参数NN参数
Define params NN params for the GridSearchCV
tuned_params = [{'solver': ['sgd'], 'learning_rate': ['constant'], "learning_rate_init" : [0.001, 0.01, 0.05, 0.1]},
{"learning_rate_init" : [0.001, 0.01, 0.05, 0.1]}]
CV方法和模型
cv_method = KFold(n_splits=4, shuffle=True)
model = MLPClassifier()
应用网格
grid = GridSearchCV(estimator=model, param_grid=tuned_params, cv=cv_method, scoring='accuracy')
grid.fit(X_train, y_train)
y_pred = grid.predict(X_test)
如果我跑步:
print(grid.best_score_)
print(accuracy_score(y_test, y_pred))
结果为 0.746478873239 和 0.902097902098
根据文档"best_score_:浮点数，剩余数据上的best_estimator分数".我认为，在KFold指定的遗留数据上，运行在 tuned_params 中指定的次数(由KFold指定的次数)中的8种不同配置所获得的精度最高.我说的对吗?
According to the doc "best_score_ : float, Score of best_estimator on the left out data". I assume it is the best accuracy among the ones obtained running the 8 different configuration as especified in tuned_params the number of times especified by KFold, on the left out data as especified by KFold. Am I right?
还有一个问题.是否有一种方法可以找到要在 train_test_split (默认值为0.25)中使用的测试数据的最佳大小?
One more question. Is there a method to find the optimal size of test data to use in train_test_split which defaults to 0.25?
非常感谢
参考
- http://scikit-learn.org/stable /modules/generation/sklearn.neural_network.MLPClassifier.html
- http://scikit -learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV
- http://scikit-learn.org/stable/modules/grid_search.html
- http://scikit-learn.org/stable/modules/cross_validation.html
- http://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html
- http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV
- http://scikit-learn.org/stable/modules/grid_search.html
- http://scikit-learn.org/stable/modules/cross_validation.html
- http://scikit-learn.org/stable/auto_examples/model_selection/plot_nested_cross_validation_iris.html#sphx-glr-auto-examples-model-selection-plot-nested-cross-validation-iris-py
推荐答案
grid.best_score_是您在tuned_params中指定的参数的单个组合的所有cv折叠的平均值.
The grid.best_score_ is the average of all cv folds for a single combination of the parameters you specify in the tuned_params.
为了访问有关网格搜索过程的其他相关详细信息，您可以查看grid.cv_results_属性.
In order to access other relevant details about the grid searching process, you can look at the grid.cv_results_ attribute.
摘自GridSearchCV的文档:
From the documentation of GridSearchCV:
cv_results_:numpy(隐藏的)ndarray的字典
cv_results_ : dict of numpy (masked) ndarrays
A dict with keys as column headers and values as columns,
that can be imported into a pandas DataFrame
它包含诸如"split0_test_score"之类的键， 'split1_test_score'， 'mean_test_score'， 'std_test_score'， 'rank_test_score'， 'split0_train_score'， 'split1_train_score'， 'mean_train_score'， 等，它提供了有关整个执行过程的其他信息.
It contains keys like 'split0_test_score', 'split1_test_score' , 'mean_test_score', 'std_test_score', 'rank_test_score', 'split0_train_score', 'split1_train_score', 'mean_train_score', etc, which gives additional information about the whole execution.
这篇关于GridSearchCV.best_score_的含义是评分设置为“准确性"和CV的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
