Skip to Main Content
Python Data Science Essentials
book

Python Data Science Essentials

by Alberto Boschetti
April 2015
Beginner content levelBeginner
258 pages
5h 48m
English
Packt Publishing
Content preview from Python Data Science Essentials

Hyper-parameters' optimization

A machine learning hypothesis is not only determined by the learning algorithm, but also by its hyper-parameters (the parameters of the algorithm that have to be a priori fixed and which cannot be learned during the training process) and the selection of variables to be used to achieve the best learned parameters.

In this section, we will explore how to extend the cross-validation approach to find the best hyper-parameters that are able to generalize to our test set. We will keep on using the handwritten digits dataset offered by the Scikit-learn package. Here's a useful reminder about how to load the dataset:

In: from sklearn.datasets import load_digits
digits = load_digits()
X, y = digits.data, digits.target

Also, ...

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Start your free trial

You might also like

Python Data Science Essentials - Second Edition

Python Data Science Essentials - Second Edition

Luca Massaron, Alberto Boschetti
Python Data Science Essentials - Third Edition

Python Data Science Essentials - Third Edition

Alberto Boschetti, Luca Massaron, Pietro Marinelli, Matteo Malosetti
Python: End-to-end Data Analysis

Python: End-to-end Data Analysis

Phuong Vothihong, Martin Czygan, Ivan Idris, Magnus Vilhelm Persson, Luiz Felipe Martins

Publisher Resources

ISBN: 9781785280429Supplemental Content