Name: Towards AI Legal Name: Towards AI, Inc. Description: Towards AI is the world's leading artificial intelligence (AI) and technology publication. Read by thought-leaders and decision-makers around the world. Phone Number: +1-650-246-9381 Email: [email protected]
228 Park Avenue South New York, NY 10003 United States
Website: Publisher: https://towardsai.net/#publisher Diversity Policy: https://towardsai.net/about Ethics Policy: https://towardsai.net/about Masthead: https://towardsai.net/about
Name: Towards AI Legal Name: Towards AI, Inc. Description: Towards AI is the world's leading artificial intelligence (AI) and technology publication. Founders: Roberto Iriondo, , Job Title: Co-founder and Advisor Works for: Towards AI, Inc. Follow Roberto: X, LinkedIn, GitHub, Google Scholar, Towards AI Profile, Medium, ML@CMU, FreeCodeCamp, Crunchbase, Bloomberg, Roberto Iriondo, Generative AI Lab, Generative AI Lab Denis Piffaretti, Job Title: Co-founder Works for: Towards AI, Inc. Louie Peters, Job Title: Co-founder Works for: Towards AI, Inc. Louis-François Bouchard, Job Title: Co-founder Works for: Towards AI, Inc. Cover:
Towards AI Cover
Logo:
Towards AI Logo
Areas Served: Worldwide Alternate Name: Towards AI, Inc. Alternate Name: Towards AI Co. Alternate Name: towards ai Alternate Name: towardsai Alternate Name: towards.ai Alternate Name: tai Alternate Name: toward ai Alternate Name: toward.ai Alternate Name: Towards AI, Inc. Alternate Name: towardsai.net Alternate Name: pub.towardsai.net
5 stars – based on 497 reviews

Frequently Used, Contextual References

TODO: Remember to copy unique IDs whenever it needs used. i.e., URL: 304b2e42315e

Resources

Unlock the full potential of AI with Building LLMs for Production—our 470+ page guide to mastering LLMs with practical projects and expert insights!

Publication

Hands-on k-fold Cross-validation for Machine Learning Model Evaluation — Cruise Ship Dataset
Latest   Machine Learning

Hands-on k-fold Cross-validation for Machine Learning Model Evaluation — Cruise Ship Dataset

Last Updated on July 20, 2023 by Editorial Team

Author(s): Benjamin Obi Tayo Ph.D.

Originally published on Towards AI.

1. Import necessary libraries

In the previous article (Feature Selection and Dimensionality Reduction Using Covariance Matrix Plot), we’ve shown that a covariance matrix plot can be used for feature selection and dimensionality reduction.

Using the cruise ship dataset cruise_ship_info.csv, we found that out of the 6 predictor features [‘age’, ‘tonnage’, ‘passengers’, ‘length’, ‘cabins’, ‘passenger_density’], if we assume important features have a correlation coefficient of 0.6 or greater with the target variable, then the target variable “crew” correlates strongly with 4 predictor variables: “tonnage”, “passengers”, “length, and “cabins”. We, therefore, were able to reduce the dimension of our feature space from 6 to 4.

Now, suppose we… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI

Feedback ↓