Author: Tayyaba Syed

k-NN (k-Nearest Neighbor), one of the simplest machine learning algorithms, is non-parametric and lazy in nature. Non-parametric means that there is no assumption for the underlying data distribution i.e. the model structure is determined from the dataset. Lazy or instance-based learning means that for the purpose of model generation, it does not require any training…

This chapter will 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 can be used for both classification as well as regression predictive problems but, it is mainly used for classification predictive problems in industry. Neighbors based learning methods do not have a…

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 data points in dataset that does not fit well with the rest of the data. It has many applications in business such as fraud detection,…

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…