Day #11 - Data Science Learning Notes - Evaluating a model

R Square - Goodness of Fit Test

• R square = (1- (Sum of Squares of Error/Sum of Squares of Total))
• SST - Variance of dependent variable
• SSE - Variance of Actual vs Predicted Values

Adjusted R Square 

• Adjusted R Square = (1-((n-1)/(n-p-1)))(1-RSquare)
• P - Number of independent variables
• n - records in dataset

RMSE (Root mean square error)

• For every record predicted compute error 
• Square it and find mean
• RMSE error should be same for training and testing dataset

Bias (Underfit)

• Model can't explain the dataset
• R Square value very less
• Add more Independent variable

Variance

• RMSE High for test dataset, RMSE low for training dataset
• Cut down Independent variable

Collinearity Problem

• Conduct P test to validate null hypothesis is valid

Next Pending Reads

• Subset Selection Technique
• Cross Validation Technique
• Z test / P Test

Happy Learning!!!