A confusion matrix is a fundamental tool in the field of machine learning and data science, often used to assess the performance of classification models. It provides a detailed breakdown of the model’s predictions compared to the actual ground truth, allowing us to evaluate various aspects of model performance. The…
Introduction In the quest to understand relationships between variables, two terms consistently surface correlation and causation. Despite their apparent similarity, they have different implications and uses. This distinction is more than just a technicality; it’s a fundamental concept that every data analyst or scientist needs to grasp. The Basics of…
Feature scaling is an important technique in Machine Learning and it is one of the most important steps during the preprocessing of data before creating a machine learning model. The reason to perform features scaling is to ensure one feature doesn’t dominate others. The two most important scaling techniques are…
Cross-validation is a resampling procedure used in machine learning to evaluate a model’s performance when the underlying data sample is limited. It involves partitioning the original training dataset into a set of ‘k’ subsets (or “folds”), training the model on a ‘k-1’ subsets, and validating the model on the remaining…