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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R, Text Mining

Text Analytics: Mining Enron Emails

Mining Enron Emails You might have heard about the Enron scandal that came to light in 2001 which eventually led to bankruptcy of the Enron corporation. This is the largest corporate fraud that had happened so far. The Enron top-honchos used what is called Mark-to-market accounting to make up their financial statements. They used this accounting and financial shenanigan to…

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Neural Networks

Activation Functions in ANNs (Conclusion)

In my last article Activation Functions in ANNs, we discussed on few activation functions, now let’s explore more on some other available activation functions. Tanh Function These are scaled sigmoid function which is similar to sigmoid functions. Or It is nonlinear so we can have more than one layer of neurons depending upon the requirement. Its range is (-1, 1).…

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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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Logistic Regression, R, Regression

Implementing Logistic Regression using Titanic dataset in R

Introduction In my last post, “Understanding mathematics behind Logistic Regression“, I explained the basic maths behind logistic regression. In this post, I intend to implement logistic regression model in R using Titanic dataset. I have used Titanic dataset for explaining logistic regression where the target variable is ‘Survived’ which has two values 0 and 1. Data Dictionary Variable Definition Key…

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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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