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
Train and cross-validate your Linear regression on Python with pre-defined or customized evaluation functions. Continue reading Linear Regression in Python
This article presents the formulas for coming up with the best-fitted linear regression line. Continue reading How to make a Linear Regressor? (theory)
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
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
Linear regression is frequently used in practice because of these 7 reasons. Continue reading Advantages 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
We tackle Regularization for Linear Regression by answering 5 questions: What, When, Where, How, and Why? Continue reading Regularization for Linear regression
This article differentiate objective functions from evaluation functions and elaborate some examples of them. Continue reading Regression Objective and Evaluation Functions
a number of different types of Linear regression based on various points of view Continue reading Types of Linear regression
Linear regression is arguably the most popular Machine learning model out there. Continue reading Introduction to Linear Regression