Regression Metrics | MAE | MSE | R2 Score | Machine Learning

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Timestamp:
00:00 -- What is Regression Metrics and its types
05:20 -- Mean Absolute Error(MAE) and its Advantage and disadvantage
19:26 -- Mean Squared Error(MSE) and its Advantage and disadvantage
24:20 -- Root Mean Squared Error(RMSE) and its Advantage and disadvantage
25:56 -- R2 Score
35:09 -- Adjusted R2 Score
43:06 -- Coding Part

Regression metrics are evaluation measures used to assess the performance and accuracy of regression models in machine learning. These metrics quantify the degree of error or discrepancy between predicted values and actual values in a continuous target variable.

Mean Squared Error (MSE): MSE calculates the average squared difference between predicted and actual values. It penalizes larger errors more heavily, making it sensitive to outliers.

Root Mean Squared Error (RMSE): RMSE is the square root of MSE and provides a measure of the average magnitude of error in the same units as the target variable. It is widely used and easy to interpret.

Mean Absolute Error (MAE): MAE calculates the average absolute difference between predicted and actual values. It provides a linear representation of the errors, disregarding their direction.

R-squared (R²) Score: R-squared measures the proportion of the variance in the target variable that can be explained by the regression model. It ranges from 0 to 1, where a higher value indicates a better fit of the model to the data.

Adjusted R-squared: Adjusted R-squared takes into account the number of predictors in the model, penalizing the addition of irrelevant predictors. It provides a more reliable measure of the model's goodness of fit compared to R-squared.

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