Evaluation Metrics for Binary-Class Classification Models — After building a machine learning model and before deploying, the model should be evaluated and tested to make sure it does its job correctly and meets the desired objectives. In this blog post, we are going to talk about some of the evaluation metrics that are used for classification models…

Know about regularization, what it is, its types and how it can reduce variance and solve overfitting. — Prerequisites: Before going on this article, make sure you have a good knowledge about bias and variance in context of machine learning and also know about overfitting. If not, please check this article first. You also need to be familiar with cost function and gradient descent. …

Overview on Bias and Variance in Machine Learning — If you are familiar with Machine Learning, you may heard about bias and variance. But if not, don’t worry, we’re going to explain them in a simple way step-by-step. Let’s use a reverse approach, we will start with a practical example and walk through until we reach the final definition. …

Clear explanation for the difference between Probability and Likelihood with a practical example — In a dictionary, you may find that “probability” and “likelihood” are usually synonyms and sometimes are used interchangeably, but from statistics’ point of view they implicitly refer to distinct things. Probability talks about the outcomes(data)/hypothesis, while likelihood talks about the model/evidence. …

Exploring Naive Bayes: Mathematics, How it works, Pros & Cons, and Applications — What is Naïve Bayes Algorithm? Naive Bayes is a classification technique that is based on Bayes’ Theorem with an assumption that all the features that predicts the target value are independent of each other. It calculates the probability of each class and then pick the one with the highest probability. …