Introduction: Random Forest Now that we have an idea about decision trees and how exactly they work, I think we can now go a step further and try to improve our decision tree models by introducing a very basic but very effective extension for decision trees, which are popularly known as “Random Forest”. To understand decision trees in detail, you…

# Category: Basics

This category covers basic concepts related to analytics. We are in the process of adding more and more basic concepts. If there’s anything specific that should be present and is missing for this category, please feel free to add a comment or drop a mail to editor@analyticsdefined.com

## All you want to know about Decision Tree Part 3

Decision Tree Part 3 This is the third article in the decision tree series, you can access other two here: Part 1: All you need to know about Decision Tree Part 1) Part 2: All you need to know about Decision Tree Part 2) In this previous article, I tried to construct a decision tree using R. For this, I have considered…

## All you need to know about Decision Tree-Part 2

Introduction In my previous article, (All you need to know about Decision Tree Part 1), we have discussed on how a decision tree looks like, its terminology and we also have seen an example of a decision tree. Now that you got a basic idea on decision tree, in this article, I will discuss on some of the major concepts…

## All you need to know about Decision Tree (Part-1)

Introduction As the title suggests, I’ll try to put necessary information on decision tree under this article. However, providing all the required information in one post will be difficult and makes you lost. So, I’ve made this article into three parts. Part 1 (this post) : we shall discuss introduction and definitions Part 2 : Advanced topics related to decision…

## Activation Functions in ANNs (Part-1)

Introduction In an ANN the activation function of a node is defined as the threshold after which the node will produce an output given an input or set of inputs. Activation functions can be linear or non-linear but mostly nonlinear functions are being used in ANNs. This is a very important in the way a network learns because in light…

## Implementing Principal Component Analysis using Python

This article is in continuation of my previous article on Mathematics of Principal Component Analysis (PCA). It is advised to go through that article before moving into this article. In this post, I will explain how to implement PCA using Python. I have taken the wholesale customer distribution dataset from UCI Machine Learning repository. This dataset refers to clients of…

## Understanding mathematics behind Logistic Regression

Introduction to Logistic Regression Logistic Regression is a type of regression in which returns the probability of occurrence of an event by fitting the data to a mathematical function called ‘logit function’. It is basically a classification algorithm and is used mostly when the dependent variable is categorical, the independent variables can be discrete or continuous. Generalized Linear Models Before starting with…

## Mathematics of Principal Component Analysis (PCA)

Understanding Principal Component Analysis In this part of the article, I will try to explain the mathematics and intuition behind Principal Component Analysis and in the next part, I will show how to implement Principal Component Analysis (PCA) using Python. PCA is an unsupervised machine learning technique which creates a low dimensional representation of a dataset. PCA is used to…