WEEK-7 ( 1/03/2021 - 5/03/2021 )

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In this week we learnt some more Model Evaluation Techniques like kappa Statistics, ROC curve and also a discussion on the implementation of Linear Regression.

Kappa Statistic: It is a measure of the agreement between two raters, in this case, the random rater and the model.
A better measure of accuracy as it compensates for chance matches
ns is the number of observed correct classifications
nR is the number of expected correct classifications
nT is the size of the test data

AUC-ROC: ROC curve is a plot of true positive rate (recall) against false positive rate (TN / (TN+FP)). AUC-ROC stands for Area Under the Receiver Operating Characteristics and the higher the area, the better is the model performance. If the curve is somewhere near the 50% diagonal line, it suggests that the model randomly predicts the output variable.
AUC-ROC curve

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