Book Image

Python Machine Learning

By : Sebastian Raschka
Book Image

Python Machine Learning

By: Sebastian Raschka

Overview of this book

Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world’s leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you’ll soon be able to answer some of the most important questions facing you and your organization.
Table of Contents (21 chapters)
Python Machine Learning
About the Author
About the Reviewers

Selecting meaningful features

If we notice that a model performs much better on a training dataset than on the test dataset, this observation is a strong indicator for overfitting. Overfitting means that model fits the parameters too closely to the particular observations in the training dataset but does not generalize well to real data—we say that the model has a high variance. A reason for overfitting is that our model is too complex for the given training data and common solutions to reduce the generalization error are listed as follows:

  • Collect more training data

  • Introduce a penalty for complexity via regularization

  • Choose a simpler model with fewer parameters

  • Reduce the dimensionality of the data

Collecting more training data is often not applicable. In the next chapter, we will learn about a useful technique to check whether more training data is helpful at all. In the following sections and subsections, we will look at common ways to reduce overfitting by regularization and dimensionality...