Machine learning is a technique of building a model by using different techniques and algorithms. Here I am not going to discuss the detail of machine learning. we will learn about the random forest algorithm of machine learning. Before we start let us take a look at the table of content.

• Introduction.
• How does the Random forest algorithm work?
• Assumption of Random Forest.
• Advantages and Disadvantages.

Introduction

Random forest is a supervised machine learning algorithm. Before we discuss the random forest we will first try to understand Bagging and the ensemble method.

The word ensemble means that combining multiple models. It…

This article was published as a part of the Data Science Blogathon.

Overview

K-means clustering is a very famous and powerful unsupervised machine learning algorithm. It is used to solve many complex unsupervised machine learning problems. Before we start let’s take a look at the points which we are going to understand.

Table Of Contents

• Introduction
• How does the K-means algorithm work?
• How to choose the value of K?
• Elbow Method.
• Silhouette Method.
• Advantages of k-means.
• Disadvantages of k-means.

Introduction

Let us understand the K-means clustering algorithm with its simple definition.

A K-means clustering algorithm tries to group similar items in the form of clusters…

Adaboost is also known as Adaptive Boosting. It is the first boosting algorithm that every machine learning enthusiast learns. This post is going to explain the in-depth explanation of AdaBoost and also the maths behind this algorithm. Now let’s start with the introduction of AdaBoost.

• AdaBoost is the ensemble learning boosting algorithm that combines several weak classifiers to create a strong classifier.
• It combines many weak decision trees and it sums the weight of all the weak decision trees to get the final result.
• The decision tree which we use to consider is called a Stump.

AdaBoost Calculation

Step-1

Let us suppose…

A decision tree is a very important supervised learning technique. It is basically a classification problem. It is a tree-shaped diagram that is used to represent the course of action. It contains the nodes and leaf nodes. it uses these nodes and leaf nodes to draw the conclusion. Here we are going to talk about the entropy in the decision tree. Let’s have a look at what we are going to learn about the decision tree entropy.

• What is Entropy?
• Importance of entropy.
• How to calculate entropy?

What is Entropy?

So let’s start with the definition of entropy. What is this entropy?

“The…

Whenever we start our journey into the Machine Learning, linear regression is the first basic algorithm which we study into the regression problem. It is a very straightforward, easy but very important algorithm. So let’s see what this linear regression is?

“The linear regression algorithm establishes the relationship between the dependent(Y) and the independent (X) variables by finding the best fit straight line.”

The equation for the linear regression is Y = mx+c

Where m is the slope and c is the intercept of the line.

In the above diagram, the straight line which you see is the best-fitted line.

Example of Linear Regression

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Everyone used to hear about the credit risk in his daily life, especially people who are working in finance, banking, or people who are working as a business analyst. So what is this credit risk? How does it occur? Well, let us understand this with the definition of credit risk.

Credit risk is the risk where the borrowers fail to repay his debt or fail to make the required payment which he had borrowed from the bank. Let suppose that you want some money to establish your business, so how will you get that? one of the options is that…