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
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
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
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
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?
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
Gradient Descent is an optimization algorithm in machine learning used to minimize a function by iteratively moving towards the minimum value of the function. Continue reading Gradient Descent — Introduction and Implementation in Python
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 — E-commerce Dataset