Scikit Learn

The Elastic-Net is a regularised regression method that linearly combines both penalties i.e. L1 and L2 of the Lasso and Ridge regression methods. It is useful when there are multiple…

In this guide, we will discuss Multi-task LASSO in Scikit-Learn. It allows to fit multiple regression problems jointly enforcing the selected features to be same for all the regression problems,…

LASSO (Least Absolute Shrinkage and Selection Operator) LASSO is the regularisation technique that performs L1 regularisation. It modifies the loss function by adding the penalty (shrinkage quantity) equivalent to the…

Bayesian ridge regression allows a natural mechanism to survive insufficient data or poorly distributed data by formulating linear regression using probability distributors rather than point estimates. The output or response…

Ridge regression or Tikhonov regularization is the regularization technique that performs L2 regularization. It modifies the loss function by adding the penalty (shrinkage quantity) equivalent to the square of the…

Logistic regression, despite its name, is a classification algorithm rather than regression algorithm. Based on a given set of independent variables, it is used to estimate discrete value (0 or…

In this topic we will discuss Linear Regression in Scikit-Learn. It is one of the best statistical models that studies the relationship between a dependent variable (Y) with a given…

This chapter will help you in learning about the linear modeling in Scikit-Learn. Let us begin by understanding what is linear regression in Sklearn. The following table lists out various…

In this guide, we will discuss Scikit-Learn Conventions. Scikit-learn’s objects share a uniform basic API that consists of the following three complementary interfaces − Estimator interface − It is for building…

In this chapter, we will learn about Scikit-Learn Estimator API (application programming interface). Let us begin by understanding what is an Estimator API. What is Estimator API It is one of the…