Archive - May 2019

Here are the steps we are going to take to understand logistic regression Basic understanding of logistic regression Some concepts that you need to understand that will aid in understanding logistic regression Basic explanation of logistic regression Understanding the maximum likelihood Bias variance tradeoff in logistic regression Regularization in logistic regression How to evaluate a logistic regression function BASIC UNDERSTANDING OF LOGISTIC REGRESSION The first thing we need to understand why we need logistic regression or generalized linear models. 1) The output of a linear regression model can be any real number ranging from negative to positive infinity, however, a categorical variable can only take on a limited number of discrete values within a specified range. 2) The error terms are not normally distributed for discrete output variables i.e 0, 1. So, the conditions for a linear regression are not met Generalized Linear Models In linear regression, we use a[…]

A decision tree can be used to visually and explicitly represent decisions and decision making. As the name goes, it uses a tree-like model of decisions In this post, these are the steps we are going to take to master just that Steps to understanding what is Decision Trees Overview of Decision Trees Some concepts that you need to understand that will aid in understanding logistic regression A basic explanation of Decision Trees Understanding maximum likelihood The bias-variance tradeoff in logistic regression Regularization in logistic regression How to evaluate a logistic regression function Overview of Decision Trees Let’s start with a simple explanation, A decision tree is a graphical representation of all the possible solutions to a decision based on certain conditions. It’s called a decision tree because it starts with a single box (or root), which then branches off into a number of solutions, just like a tree. For[…]