 # Ensemble: Bagging, Random Forest, Boosting and Stacking

An ensemble of trees (in the form of bagging, random forest, or boosting) is usually preferred over one decision tree alone. Continue reading Ensemble: Bagging, Random Forest, Boosting and Stacking # Linear Regression in Python

Train and cross-validate your Linear regression on Python with pre-defined or customized evaluation functions. Continue reading Linear Regression in Python # How to make a Linear Regressor? (theory)

This article presents the formulas for coming up with the best-fitted linear regression line. Continue reading How to make a Linear Regressor? (theory) # Confidence Intervals for Linear Regression Coefficients

Not only does Linear regression give us a model for prediction, but it also tells us about how accurate the model is, by the means of Confidence Intervals. Continue reading Confidence Intervals for Linear Regression Coefficients # Disadvantages of Linear Regression

If you are considering using Linear regression for your production pipeline, you should be aware of its 4 drawbacks. Continue reading Disadvantages of Linear Regression # Advantages of Linear Regression

Linear regression is frequently used in practice because of these 7 reasons. Continue reading Advantages of Linear Regression # Assumptions of Linear Regression

In which cases does Linear Regression perform the best? In which cases should we use other algorithms? Continue reading Assumptions of Linear Regression # Regularization for Linear regression

We tackle Regularization for Linear Regression by answering 5 questions: What, When, Where, How, and Why? Continue reading Regularization for Linear regression # Regression Objective and Evaluation Functions

This article differentiate objective functions from evaluation functions and elaborate some examples of them. Continue reading Regression Objective and Evaluation Functions # Introduction to Linear Regression

Linear regression is arguably the most popular Machine learning model out there. Continue reading Introduction to Linear Regression