Overfitting

Using Optimal Subsampling and Bootstrap in Machine Learning: Improving Model Accuracy

Introduction Machine learning algorithms have become increasingly popular in recent years as businesses and organizations look for ways to extract value from their data. However, getting accurate predictions from machine learning models is not always easy. One of the biggest challenges is reducing overfitting and bias in the models, which can lead to inaccurate predictions. …

Using Optimal Subsampling and Bootstrap in Machine Learning: Improving Model Accuracy Read More »

Big Data, Big Models: How to Train and Optimize Large Scale Sparse Models

Introduction Big data has become an integral part of modern business and research, with vast amounts of information being collected, analyzed, and stored every day. With the increasing volume of data, the need for more powerful models to analyze it has also grown. However, training large scale models can be a challenging task, especially when …

Big Data, Big Models: How to Train and Optimize Large Scale Sparse Models Read More »

Penalized Two-Pass Regression: A Step-by-Step Guide

Introduction Penalized regression is a technique used in machine learning and statistics to improve the performance of linear regression models. One specific variation of penalized regression is known as two-pass regression, which involves two stages of variable selection and regularization. In this blog post, we will discuss the concept of penalized two-pass regression, its advantages, …

Penalized Two-Pass Regression: A Step-by-Step Guide Read More »