DataSeries
Published in

DataSeries

Cross-Validation for model selection

Why it is important for obtaining well-generalized models

When you are dealing with a Machine Learning task, you have to properly identify your problem so that you can pick the most suitable algorithm. As first thing, namely, you could categorize your task either as supervised or unsupervised and, if supervised, either as classification or as regression (you can read more about it here).

However, this does not lead to a unique solution, since multiple algorithms exist for each category of learning…

--

--

--

Imagine the future of data

Recommended from Medium

The Advantages of Using ML for Behavioral Analysis in Health Care, IT Security, and More

Behavioral Analysis

Text Mining: Word Vectorization Techniques

Computer Vision: Face Mask Detection with CNN

One reason? Warming has had an economically neutral or positive impact in many of the world’s

‘Fault’ Detection a.k.a Prognostic Health Management and Test Methodologies

Has Sci-Fi predicted all?

Understanding mean Average Precision for Object Detection (with Python Code)

Building a great ML platform using Jupyter Hub, SageMaker and spark on AWS.

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
Valentina Alto

Valentina Alto

Cloud Specialist at @Microsoft | MSc in Data Science | Machine Learning, Statistics and Running enthusiast

More from Medium

Simple Explainable Machine Learning

What is gradient boosted regression?

Learning Ensemble methods