Categories: AI/ML Research

Decision Trees and Ordinal Encoding: A Practical Guide

Categorical variables are pivotal as they often carry essential information that influences the outcome of predictive models. However, their non-numeric nature presents unique challenges in model processing, necessitating specific strategies for encoding. This post will begin by discussing the different types of categorical data often encountered in datasets. We will explore ordinal encoding in-depth and […]

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One Hot Encoding: Understanding the “Hot” in Data

Preparing categorical data correctly is a fundamental step in machine learning, particularly when using linear models. One Hot Encoding stands out as a key technique, enabling the transformation of categorical variables into a machine-understandable format. This post tells you why you cannot use a categorical variable directly and demonstrates the…

August 19, 2024

In "AI/ML Research"

Refit trained parameters on large datasets using Amazon SageMaker Data Wrangler

November 15, 2022

In "FAANG"

Generative AI models are encoding biases and negative stereotypes in their users, say researchers

In the space of a few months generative AI models, such as ChatGPT, Google's Bard and Midjourney, have been adopted by more and more people in a variety of professional and personal ways. But growing research is underlining that they are encoding biases and negative stereotypes in their users, as…

June 23, 2023

In "AI/ML News"

AI Generated Robotic Content