AIC (Akaike Information Criterion) and BIC (Bayesian Information Criterion) are essential tools for model selection in statistical learning. Both balance model complexity and goodness of fit, helping to avoid overfitting. AIC favors models that best explain the data, while BIC prefers simpler models with a stronger penalty for complexity. This guide explains their differences, when to use each, and how to calculate them in Python, making it easier to choose the best model for your data analysis. Read more

Statistical learning is a field that involves the use of statistics to understand and predict data patterns. It provides a set of tools for modeling and understanding complex datasets, widely used in machine learning and data science. Here’s a breakdown of the key concepts and a step-by-step tutorial on statistical learning Read more