We used random forests to build a classifier in the previous recipe, but we don't exactly know how to define the parameters. In our case, we dealt with two parameters: n_estimators
and max_depth
. They are called hyperparameters, and the performance of the classifier depends on them. It would be nice to see how the performance gets affected as we change the hyperparameters. This is where validation curves come into picture. These curves help us understand how each hyperparameter influences the training score. Basically, all other parameters are kept constant and we vary the hyperparameter of interest according to our range. We will then be able to visualize how this affects the score.
Python Machine Learning Cookbook
By :
Python Machine Learning Cookbook
By:
Overview of this book
Machine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more.
With this book, you will learn how to perform various machine learning tasks in different environments. We’ll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the book, you’ll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms.
You’ll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. We also cover a range of regression techniques, classification algorithms, predictive modeling, data visualization techniques, recommendation engines, and more with the help of real-world examples.
Table of Contents (19 chapters)
Python Machine Learning Cookbook
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Free Chapter
The Realm of Supervised Learning
Constructing a Classifier
Predictive Modeling
Clustering with Unsupervised Learning
Building Recommendation Engines
Analyzing Text Data
Speech Recognition
Dissecting Time Series and Sequential Data
Image Content Analysis
Biometric Face Recognition
Deep Neural Networks
Visualizing Data
Index
Customer Reviews