- Scikit Learn Tutorial
- Scikit Learn - Home
- Scikit Learn - Introduction
- Scikit Learn - Modelling Process
- Scikit Learn - Data Representation
- Scikit Learn - Estimator API
- Scikit Learn - Conventions
- Scikit Learn - Linear Modeling
- Scikit Learn - Extended Linear Modeling
- Stochastic Gradient Descent
- Scikit Learn - Support Vector Machines
- Scikit Learn - Anomaly Detection
- Scikit Learn - K-Nearest Neighbors
- Scikit Learn - KNN Learning
- Classification with Naïve Bayes
- Scikit Learn - Decision Trees
- Randomized Decision Trees
- Scikit Learn - Boosting Methods
- Scikit Learn - Clustering Methods
- Clustering Performance Evaluation
- Dimensionality Reduction using PCA
- Scikit Learn Useful Resources
- Scikit Learn - Quick Guide
- Scikit Learn - Useful Resources
- Scikit Learn - Discussion
Scikit Learn - Linear Modeling
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 linear models provided by Scikit-Learn −
|Sr.No||Model & Description|
It is one of the best statistical models that studies the relationship between a dependent variable (Y) with a given set of independent variables (X).
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 1, yes/no, true/false).
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 magnitude of coefficients.
Bayesian regression allows a natural mechanism to survive insufficient data or poorly distributed data by formulating linear regression using probability distributors rather than point estimates.
LASSO is the regularisation technique that performs L1 regularisation. It modifies the loss function by adding the penalty (shrinkage quantity) equivalent to the summation of the absolute value of coefficients.
It allows to fit multiple regression problems jointly enforcing the selected features to be same for all the regression problems, also called tasks. Sklearn provides a linear model named MultiTaskLasso, trained with a mixed L1, L2-norm for regularisation, which estimates sparse coefficients for multiple regression problems jointly.
The Elastic-Net is a regularized 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 correlated features.
It is an Elastic-Net model that allows to fit multiple regression problems jointly enforcing the selected features to be same for all the regression problems, also called tasks
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