You're reading from Mastering Predictive Analytics with scikit-learn and TensorFlow

Product typeBook

Published inSep 2018

Reading LevelIntermediate

PublisherPackt

ISBN-139781789617740

Edition1st Edition

Languages

Python

Tools

Scikit-learn TensorFlow

Concepts

Predictive Analytics

Author (1)

Alvaro Fuentes

Introduction to hyperparameter tuning

The method used to choose the best estimators for a particular dataset or choosing the best values for all hyperparameters is called hyperparameter tuning. Hyperparameters are parameters that are not directly learned within estimators. Their value is decided by the modelers.

For example, in the RandomForestClassifier object, there are a lot of hyperparameters, such as n_estimators, max_depth, max_features, and min_samples_split. Modelers decide the values for these hyperparameters.

Exhaustive grid search

One of the most important and generally-used methods for performing hyperparameter tuning is called the exhaustive grid search. This is a brute-force approach because it tries all of...

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Mastering Predictive Analytics with scikit-learn and TensorFlow

Published in: Sep 2018Publisher: PacktISBN-13: 9781789617740

Author (1)

Alvaro Fuentes

Alvaro Fuentes is a senior data scientist with a background in applied mathematics and economics. He has more than 14 years of experience in various analytical roles and is an analytics consultant at one of the ‘Big Three' global management consulting firms, leading advanced analytics projects in different industries like banking, technology, and consumer goods. Alvaro is also an author and trainer in analytics and data science and has published courses and books, such as 'Become a Python Data Analyst' and 'Hands-On Predictive Analytics with Python'. He has also taught data science and related topics to thousands of students both on-site and online through different platforms such as Springboard, Simplilearn, Udemy, and BSG Institute, among others.
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Author (1)

Hands-On Predictive Analytics with Python

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