Course Content

  • 5_6_Evaluating_Regression_Model_Performance

Course Content

FAQs

In regression, there are three main metrics for model evaluation:

  • R Square/R Square Adjusted
  • MSE/MSE/MSE/MSE/MSE/MSE/MSE/MSE/MSE/MSE/MSE/ (RMSE)
  • Absolute Mean Error (MAE)

Plotting the predicted values against the real values in the holdout set is the best way to examine regression data. In a perfect world, the points would be on the 45-degree line flowing through the origin (y = x is the equation). The better the regression, the closer the points are to this line.

The technique of regression is used to investigate the relationship between independent variables or features and a dependent variable or result. It is used in machine learning as a method for predictive modeling, in which an algorithm is employed to predict continuous outcomes.

Regression analysis is an effective statistical tool for examining the relationship between two or more variables of interest. While there are many different types of regression analysis, they always focus on the impact of one or more independent variables on a dependent variable.

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