Category: Tutorial

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, regression, and, outliers’ detection. SVMs are very efficient in high dimensional spaces and generally are used in classification problems. SVMs are popular and memory efficient…

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 the values of parameters/coefficients of functions that minimize a cost function. In other words, it is used for discriminative learning of linear classifiers under convex…

This chapter focusses on the polynomial features and pipelining tools in Sklearn. Introduction to Polynomial Features Linear models trained on non-linear functions of data generally maintains the fast performance of linear methods. It also allows them to fit a much wider range of data. That’s the reason in machine learning such linear models, that are…

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 1 Linear RegressionIt is one of the best statistical models that studies the relationship between…

Scikit-learn’s objects share a uniform basic API that consists of the following three complementary interfaces − The APIs adopt simple conventions and the design choices have been guided in a manner to avoid the proliferation of framework code. Purpose of Conventions The purpose of conventions is to make sure that the API stick to the…

In this chapter, we will learn about Estimator API (application programming interface). Let us begin by understanding what is an Estimator API. What is Estimator API It is one of the main APIs implemented by Scikit-learn. It provides a consistent interface for a wide range of ML applications that’s why all machine learning algorithms in Scikit-Learn are…

As we know that machine learning is about to create model from data. For this purpose, computer must understand the data first. Next, we are going to discuss various ways to represent the data in order to be understood by computer − Data as table The best way to represent data in Scikit-learn is in…

This chapter deals with the modelling process involved in Sklearn. Let us understand about the same in detail and begin with dataset loading. Dataset Loading A collection of data is called dataset. It is having the following two components − Features − The variables of data are called its features. They are also known as predictors,…

In this chapter, we will understand what is Scikit-Learn or Sklearn, origin of Scikit-Learn and some other related topics such as communities and contributors responsible for development and maintenance of Scikit-Learn, its prerequisites, installation and its features. What is Scikit-Learn (Sklearn) Scikit-learn (Sklearn) is the most useful and robust library for machine learning in Python.…

Scikit-learn (Sklearn) is the most useful and robust library for machine learning in Python. It provides a selection of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction via a consistence interface in Python. This library, which is largely written in Python, is built upon NumPy, SciPy and Matplotlib.…