如何为 K 折交叉验证计算不平衡数据集的精度、召回率和 f1 分数? [英] How to compute precision,recall and f1 score of an imbalanced dataset for K fold cross validation?

问题描述

我有一个包含二元分类问题的不平衡数据集.我构建了随机森林分类器并使用了 10 折的 k 折交叉验证.

I have an imbalanced dataset containing binary classification problem.I have built Random Forest Classifier and used k fold cross validation with 10 folds.

kfold = model_selection.KFold(n_splits=10, random_state=42)
model=RandomForestClassifier(n_estimators=50) 

我得到了10折的结果

results = model_selection.cross_val_score(model,features,labels, cv=kfold)
print results
[ 0.60666667  0.60333333  0.52333333  0.73        0.75333333  0.72        0.7
  0.73        0.83666667  0.88666667]

我通过计算结果的均值和标准差来计算准确度

I have calculated accuracy by taking mean and standard deviation of the results

print("Accuracy: %.3f%% (%.3f%%)") % (results.mean()*100.0, results.std()*100.0)
Accuracy: 70.900% (10.345%)

我的预测计算如下

predictions = cross_val_predict(model, features,labels ,cv=10)

由于这是一个不平衡的数据集，我想计算每个折叠的精度、召回率和 f1 分数并对结果求平均值.如何计算python中的值?

Since this is an imbalanced dataset,I would like to calculate precision,recall and f1 score of each fold and average the results. How to calculate the values in python?

推荐答案

当你使用 cross_val_score 方法时，你可以指定，你可以计算每个折叠的得分:

When you use cross_val_score method, you can specify, which scorings you can calculate on each fold:

from sklearn.metrics import make_scorer, accuracy_score, precision_score, recall_score, f1_score

scoring = {'accuracy' : make_scorer(accuracy_score), 
           'precision' : make_scorer(precision_score),
           'recall' : make_scorer(recall_score), 
           'f1_score' : make_scorer(f1_score)}

kfold = model_selection.KFold(n_splits=10, random_state=42)
model=RandomForestClassifier(n_estimators=50) 

results = model_selection.cross_val_score(estimator=model,
                                          X=features,
                                          y=labels,
                                          cv=kfold,
                                          scoring=scoring)

交叉验证后，您将获得带有键的 results 字典:'accuracy'、'precision'、'recall'、'f1_score'，这些键存储特定度量的每个折叠的度量值.对于每个指标，您可以使用 np.mean(results[value]) 和 np.std(results[value]) 计算均值和标准值，其中值 - 一您指定的指标名称.

After cross validation, you will get results dictionary with keys: 'accuracy', 'precision', 'recall', 'f1_score', which store metrics values on each fold for certain metric. For each metric you can calculate mean and std value by using np.mean(results[value]) and np.std(results[value]), where value - one of your specified metric name.

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