# Naive Bayes Classifier

We will look at what Naive Bayes Classifier is, little bit of math behind it, which applications are Naive Bayes Classifier typically used for, and finally an example of SMS Spam Filter using Naive Bayes Classifier. Continue reading Naive Bayes Classifier

# Logistic Regression – Explained

Introduction In this post we will be exploring and understanding one of the basic Classification Techniques in Machine Learning – Logistic Regression. Binary logistic regression: It has only two possible outcomes. Example- yes or no Multinomial logistic regression: It has … Continue reading Logistic Regression – Explained

# Machine Learning – Classification

Classification can be easily defined as – ‘To identify which category/class the new set of data belongs to, on the basis of set of data which is already classified.’ Continue reading Machine Learning – Classification

# Assumptions Of Linear Regression – How to Validate and Fix

Linear Regression makes certain assumptions about the data and provides predictions based on that. Naturally, if we don’t take care of those assumptions Linear Regression will penalise us with a bad model (You can’t really blame it!). Continue reading Assumptions Of Linear Regression – How to Validate and Fix

# Multicollinearity – How to fix it?

This post will answer questions like What is multicollinearity ?, What are the problems that arise out of Multicollinearity? When do I have to fix Multicollinearity? and How to fix Multicollinearity? One of the important aspect that we have to … Continue reading Multicollinearity – How to fix it?

# Feature Elimination Using p-values

In statistical hypothesis testing, the p-value or probability value is the probability of obtaining test results at least as extreme as the results actually observed during the test, assuming that the null hypothesis is correct. Continue reading Feature Elimination Using p-values

# Introduction To Linear Regression â€” E-commerce Dataset

Linear regression model is used to predict the relationship between variables or factors. The factor that is being predicted is called the scalar response (or dependent variable). The factors that are used to predict the value of the dependent variable are called explanatory variables (or independent variables). Continue reading Introduction To Linear Regression â€” E-commerce Dataset