This chapter we will discuss K-Nearest Neighbors (KNN) and help you in understanding the nearest neighbor methods in Sklearn. Neighbor based learning method are of both types namely supervised and unsupervised. Supervised neighbors-based learning…
Here, we will learn about what is anomaly detection in Sklearn and how it is used in identification of the data points. Anomaly detection is a technique used to identify…
This chapter deals with a machine learning method termed as Support Vector Machines (SVMs). Introduction Support vector machines (SVMs) are powerful yet flexible supervised machine learning methods used for classification,…
Here, we will learn about an optimization algorithm in Sklearn, termed as Stochastic Gradient Descent (SGD). Stochastic Gradient Descent (SGD) is a simple yet efficient optimization algorithm used to find…
This chapter, we will discuss Extended Linear Modeling and focusses on the polynomial features and pipelining tools in Sklearn. Introduction to Polynomial Features Linear models trained on non-linear functions of…
Multi Task Elastic Net 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…
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…