Precision | Recall | Accuracy | F1 score | ML model evaluation parameters

Precision | Recall | Accuracy | F1 score

Evaluation of a machine learning model is based on four parameters and that are - 

1. Accuracy
2. Recall
3. precision
4. F1 score

For understanding of above mentioned four parameters, firstly we need to understand what is confusion metrics?

Confusion metrics - 

It is a two dimensional metrics and these two parameters are actual results and prediction results. It is used for describing the performance of a machine learning classification model with the help of True and False values.

It contain four type of values.

For understanding of below mentioned terminologies, let's take an example - "suppose a person is in the doctor clinic and want to know whether he is suffering from fever or his health is normal. so what are the possibilities of results."

TN (True Negative) Neither person actually suffering from fever nor doctor predict ,he is suffering.

TP (True Positive) person actually suffering from fever as well as doctor predict ,he is suffering

FP (False Positive) person is actually not suffering from fever but doctor predict, he is suffering.

FN (False Negative) person is actually suffering from fever but doctor predict, he is not suffering.

With the help of above mentioned (TN, TP, FP, FN), we evaluate all parameters.

Precision -

Precision is the ration of  'True Positives'  with predicted 'Positives'. As clear from the above figure i.e; predicted positives contains True Positives and False Positives.

formula - TP / (TP + FP)

Recall -

Recall is the ratio of  'True Positives' with Actual 'Positives'. And actual positives contains True Positives and False Negatives.

formula - TP/ (TP + FN)

Accuracy -

It is the ratio of  'True Positives' and 'True Negatives' with Total observations. 

formula - (TP + TN)  /  (TP + TN + FN + FP)

F1 score -

F1 score is the weighted average of Precision and Recall and can be calculated by following formula.

F1 score = 2*(Recall * Precision) / (Recall + Precision)

Accuracy and F1 score both are quite similar but F1 score is more useful than accuracy because accuracy works better when False Negative and False Positive have similar costs(value) so on that condition we should take F1 score for model selection.

I hope that now you have better understanding regarding terminologies for machine learning model evaluation.

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