Understanding a Classification Report For Your Machine Learning Model
The classification report visualizer displays the precision, recall, F1, and support scores for the model.
There are four ways to check if the predictions are right or wrong:
- TN / True Negative: the case was negative and predicted negative
- TP / True Positive: the case was positive and predicted positive
- FN / False Negative: the case was positive but predicted negative
- FP / False Positive: the case was negative but predicted positive
Precision — What percent of your predictions were correct?
Precision is the ability of a classifier not to label an instance positive that is actually negative. For each class, it is defined as the ratio of true positives to the sum of a true positive and false positive.
Precision:- Accuracy of positive predictions.
Precision = TP/(TP + FP)
Recall — What percent of the positive cases did you catch?
Recall is the ability of a classifier to find all positive instances. For each class it is defined as the ratio of true positives to the sum of true positives and false negatives.
Recall:- Fraction of positives that were correctly identified.
Recall = TP/(TP+FN)
F1 score — What percent of positive predictions were correct?
The F1 score is a weighted harmonic mean of precision and recall such that the best score is 1.0 and the worst is 0.0. F1 scores are lower than accuracy measures as they embed precision and recall into their computation. As a rule of thumb, the weighted average of F1 should be used to compare classifier models, not global accuracy.
F1 Score = 2*(Recall * Precision) / (Recall + Precision)
Support
Support is the number of actual occurrences of the class in the specified dataset. Imbalanced support in the training data may indicate structural weaknesses in the reported scores of the classifier and could indicate the need for stratified sampling or rebalancing. Support doesn’t change between models but instead diagnoses the evaluation process.
