Author: S K S

This chapter will help you in understanding randomized decision trees in Sklearn. Randomized Decision Tree algorithms As we know that a DT is usually trained by recursively splitting the data,…

In this guide, we will learn about learning method in Sklearn which is termed as decision trees. Decisions tress (DTs) are the most powerful non-parametric supervised learning method. They can…

In this guide, we will discuss Complement Naïve Bayes in Scikit-Learn. Another useful naïve Bayes model which was designed to correct the severe assumptions made by Multinomial Bayes classifier. This…

Bernoulli Naïve Bayes is another useful naïve Bayes model. The assumption in this model is that the features binary (0s and 1s) in nature. An application of Bernoulli Naïve Bayes…

In this guide, we will discuss Multinomial Naïve Bayes in Scikit-Learn. It is another useful Naïve Bayes classifier. It assumes that the features are drawn from a simple Multinomial distribution.…

In this guide, we will discuss Gaussian Naïve Bayes in Scikit-Learn. As the name suggest, Gaussian Naïve Bayes classifier assumes that the data from each label is drawn from a…

In this guide, we will discuss Classification with Naïve Bayes in Scikit-Learn. Naïve Bayes methods are a set of supervised learning algorithms based on applying Bayes’ theorem with a strong…

In this guide, we will discuss RadiusNeighborsClassifier in Scikit-Learn. The Radius in the name of this classifier represents the nearest neighbors within a specified radius r, where r is a…

In this guide, we will discuss KNeighborsClassifier in Scikit-Learn. The K in the name of this classifier represents the k nearest neighbors, where k is an integer value specified by…

In this chapter , We will discuss KNN Learning in Scikit-Learn. k-NN (k-Nearest Neighbor), one of the simplest machine learning algorithms, is non-parametric and lazy in nature. Non-parametric means that…