Since State is categorical variable => we need to convert it into dummy variable.(Back to top) Multiple Linear Regression Assumptions of Linear Regression:īefore choosing Linear Regression, need to consider below assumptions metrics import mean_absolute_error print( mean_absolute_error( Y_test, Y_predicted)) In Python, you can calculate R Square using Statsmodel or Sklearn Packageįrom sklearn.Will penalise additional independent variables added to the model and adjust the metric to prevent overfitting issue.Ĭalculate R Square and Adjusted R Square using Python.is introduced Since R-square can be increased by adding more number of variable and may lead to the over-fitting of the model.If your regression model has many independent variables, because the model is too complicated, it may fit very well to the training data.However, it does not take into consideration of overfitting problem.R Square value: between 0 to 1 and bigger value indicates a better fit between prediction and actual value.Variance is a measure in statistics defined as the average of the square of differences between individual point and the expected value.R Square measures how much of variability in predicted variable can be explained by the model.R Square or Adjusted R Square R Square: Coefficient of determination Mean Absolute Error(MAE) > to compare performance between different regression models.Mean Square Error(MSE)/Root Mean Square Error(RMSE) > to compare performance between different regression models.R Square/Adjusted R Square > Percentage of the output variability.For regression algorithms, three evaluation metrics are commonly used:.compare how well different algorithms perform on a particular dataset.Linear Regression Equation: Salary = 25609 + 9332.94×YearsExperience Evaluating the Algorithm Getting the final linear regression equation (with values of the coefficients).Predicting and visualizing the test set results.Training the simple Linear Regression model on the Training set.
Simple Linear Regression Outline Building a Model Polynomial Linear Regression: y = b0 + b1*x1 + b2*x1^(2) +.Multiple Linear Regression : y = b0 + b1*x1 + b2*x2 +.Simple Linear Regression : y = b0 + b1*x1.Mean Square Error (MSE)/Root Mean Square Error (RMSE).
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