Random Forests
Basics, R, Supervised Learning

Introduction to Random Forest

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…

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Inverted tree image
Basics, Supervised Learning

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…

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Activation Function
Basics, Neural Networks

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…

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Basics, Unsupervised Learning

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…

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Basics, Regression

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…

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Basics, Fun with Statistics, Unsupervised Learning

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…

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