My previous article was about Confusion Matrix where we discussed the importance of it, how is it read and calculated and what are the implications of having imbalanced data, and how recall and precision values come to the rescue for the evaluation of an imbalanced classification problem. Starting with an…

Confusion Matrix is one of the evaluation metrics for machine learning classification problems, where a trained model is being evaluated for accuracy and other performance measures. …

In this article, I would like to cover one of the most important but most forgotten part of Machine Learning, that is, Feature Scaling. While we are working on our data preparation or EDA (Exploratory Data Analysis) we kind of focus a lot on all variables/features we have, we treat…

Covariance and Correlation are the terms you would come across very frequently while you are building models in machine learning. Conceptually it is very important to understand what these terms mean. Through this article we will see: What these two terms are? How Covariance and Correlation are different from each…

Hierarchical Clustering is one of the most extensively used clustering techniques in unsupervised machine learning. It is a bit different from K-Means Clustering and we will explore the main core concept behind it and the key differences between various types of hierarchical clustering. Clustering is a technique that is used…

Ensemble Methods in Machine Learning relies on a very basic hypothesis that if we combine multiple models (weak learners) together it will result in a powerful model. In this article, I would again try and explain the core concept behind the ensemble methods without getting too much into technicalities, since…

The Lambda (𝜆) Parameter also known as the regularization rate is used to tune the overall impact of regularization on the complexity error. I have already covered the complexity error and regularization in one of my previous articles. The idea behind this is that we use this parameter to penalize…

Regularization is one of the key technique which is used in Regression to address the overfitting and complexity issue if too many variables are present in the dataset. Again I would try to cover up the basic concept behind the regularization technique without getting into too many technicalities. In Regression…

SVM is one of the most important but difficult algorithms when it comes to understanding all the jargon it has. Through this article, I would like to discuss a few key concepts which build this algorithm. …