A confusion matrix is a table used to evaluate the performance of a classification model. It can be used to measure the accuracy of a model in predicting the correct class for each data point. To ...

A confusion matrix is a table that shows the performance of a classification model. It is used to evaluate the accuracy of a model by comparing its predictions to the actual values. The rows of the ...

A confusion matrix enables the calculation of key metrics like accuracy, precision, recall, and F1-score, providing comprehensive insights into model performance.

In such scenarios, you may be surprised to see the accuracy of the model peaking at 99% but in reality, the model is highly biased towards the dominant class. There is very little possibility that you ...