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Binary Class Evaluation Metrices.
Evaluation Matrixes Table Of Contents: Accuracy Precision Recall (TPR, Sensitivity) Specificity (TNR) F1-Score FPR (Type I Error) FNR (Type II Error) (1) Accuracy: Accuracy simply measures how often the classifier makes the correct prediction. It’s the ratio between the number of correct predictions and the total number of predictions. The accuracy metric is not suited for imbalanced classes. Accuracy has its own disadvantages, for imbalanced data, when the model predicts that each point belongs to the majority class label, the accuracy will be high. But, the model is not accurate. It is a measure of correctness that is achieved in true prediction. In simple words, it tells us how many predictions are actually positive out
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Confusion Metrix
Confusion Metrix Table Of Contents: What Is Confusion Metrix? Why We Need Confusion Metrix? Elements Of Confusion Metrix. Examples Of Confusion Metrix. Evaluation Matrixes. (1) Why We Need Confusion Metrix? In machine learning, Classification is used to split data into categories. But after cleaning and preprocessing the data and training our model, how do we know if our classification model performs well? That is where a confusion matrix comes into the picture. A confusion matrix is used to measure the performance of a classifier in depth. (2) What Is Confusion Metrix? A confusion matrix, also known as an error matrix,
