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我正在做一个项目，我需要计算 gridsearch 返回的最佳估计量。

parameters = {'gamma':[0.1, 0.5, 1, 10, 100], 'C':[1, 5, 10, 100, 1000]}

# TODO: Initialize the classifier
svr = svm.SVC()

# TODO: Make an f1 scoring function using 'make_scorer' 
f1_scorer = make_scorer(score_func)

# TODO: Perform grid search on the classifier using the f1_scorer as the scoring method
grid_obj = grid_search.GridSearchCV(svr, parameters, scoring=f1_scorer)

# TODO: Fit the grid search object to the training data and find the optimal parameters
grid_obj = grid_obj.fit(X_train, y_train)
pred = grid_obj.predict(X_test)
def score_func():
    f1_score(y_test, pred, pos_label='yes')

# Get the estimator
clf = grid_obj.best_estimator_

我不确定如何使 f1_scorer 函数发挥作用，因为我在创建 gridsearch 对象后进行了预测。创建 obj 后我无法声明 f1_scorer，因为 gridsearch 使用它作为评分方法。请帮助我如何为网格搜索创建这个评分函数。

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2 回答 2

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您传递给的记分器函数make_scorer应采用y_true 和y_pred作为参数。有了这些信息，您就拥有了计算分数所需的一切。然后 GridSearchCV 将为每个可能的参数集在内部为您拟合并调用 score 函数，您无需事先计算 y_pred。

它应该如下所示：

def score_func(y_true, y_pred):
    """Calculate f1 score given the predicted and expected labels"""
    return f1_score(y_true, y_pred, pos_label='yes')

f1_scorer = make_scorer(score_func)
GridSearchCV(svr, parameters, scoring=f1_scorer)
于 2016-06-09T10:13:42.077 回答
0 
clf = svm.SVC()

# TODO: Make an f1 scoring function using 'make_scorer' 
f1_scorer = make_scorer(f1_score,pos_label='yes')

# TODO: Perform grid search on the classifier using the f1_scorer as the scoring method
grid_obj = GridSearchCV(clf,parameters,scoring=f1_scorer)

# TODO: Fit the grid search object to the training data and find the optimal parameters
grid_obj = grid_obj.fit(X_train, y_train)

# Get the estimator
clf = grid_obj.best_estimator_
于 2016-06-14T12:42:00.823 回答