How to compute precision,recall and f1 score of an imbalanced dataset for K fold cross validation?

Question

I have an imbalanced dataset containing a 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)

I got the results of the 10 folds

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]

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%)

I have computed my predictions as follows

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

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

Asked By: Jayashree

||

Source

Answer 1

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)

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.

Answered By: Eduard Ilyasov

Answer 2

All of the scores you mentioned — accuracy, precision, recall and f1 — rely on the threshold you (manually) set for the prediction to predict the class. If you don’t specify a threshold, the default threshold is 0.5, see here. The threshold should always be set according to the cost of misclassification. If no cost is given, you should make an assumption.

In order to be able to compare different models or hyperparameters, you might consider using the Area Under Curve (AUC) for the Precision Recall Curve since it is independent of the threshold by showing precision and recall for different thresholds. In your specific case of imbalanced data, the PR-AUC is more appropriate then the AUC for the ROC, see here.

See also here: https://datascience.stackexchange.com/a/96708/131238

Answered By: DataJanitor

How to compute precision,recall and f1 score of an imbalanced dataset for K fold cross validation?

Question:

Answers: