Categories: AI/ML Research

Scaling to Success: Implementing and Optimizing Penalized Models

This post will demonstrate the usage of Lasso, Ridge, and ElasticNet models using the Ames housing dataset. These models are particularly valuable when dealing with data that may suffer from multicollinearity. We leverage these advanced regression techniques to show how feature scaling and hyperparameter tuning can improve model performance. In this post, we’ll provide a […]

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Filling the Gaps: A Comparative Guide to Imputation Techniques in Machine Learning

In our previous exploration of penalized regression models such as Lasso, Ridge, and ElasticNet, we demonstrated how effectively these models manage multicollinearity, allowing us to utilize a broader array of features to enhance model performance. Building on this foundation, we now address another crucial aspect of data preprocessing—handling missing values.…

September 14, 2024

In "AI/ML Research"

Implementing Advanced Feature Scaling Techniques in Python Step-by-Step

In this article, you will learn: • Why standard scaling methods are sometimes insufficient and when to use advanced techniques.

July 31, 2025

In "AI/ML Research"

Optimizing Machine Learning Models for Production: A Step-by-Step Guide

This article provides a comprehensive step-by-step guide designed to help you navigate the challenge of optimizing your machine learning (ML) models for production, by looking at all stages in their development lifecycle, i.

December 10, 2024

In "AI/ML Research"

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