Feature engineering plays a vital role in making machine learning algorithms work and, if carried out properly, it enhances the predictive ability of machine learning algorithms. In other words, feature engineering is the process of extracting existing features or creating new features from the raw data using domain knowledge, the context of the problem, or specialized techniques that result in more accurate predictive models. This is an activity where domain knowledge and creativity play a very important role. This is an important process, which can significantly improve the performance of our predictive models. The more context you have about a problem, the better your ability to create new and useful features. Basically, the feature engineering process converts the features into input values that algorithms can understand.
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Mastering Predictive Analytics with scikit-learn and TensorFlow
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Mastering Predictive Analytics with scikit-learn and TensorFlow
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Overview of this book
Python is a programming language that provides a wide range of features that can be used in the field of data science. Mastering Predictive Analytics with scikit-learn and TensorFlow covers various implementations of ensemble methods, how they are used with real-world datasets, and how they improve prediction accuracy in classification and regression problems.
This book starts with ensemble methods and their features. You will see that scikit-learn provides tools for choosing hyperparameters for models. As you make your way through the book, you will cover the nitty-gritty of predictive analytics and explore its features and characteristics. You will also be introduced to artificial neural networks and TensorFlow, and how it is used to create neural networks. In the final chapter, you will explore factors such as computational power, along with improvement methods and software enhancements for efficient predictive analytics.
By the end of this book, you will be well-versed in using deep neural networks to solve common problems in big data analysis.
Table of Contents (7 chapters)
Preface
Free Chapter
Ensemble Methods for Regression and Classification
Cross-validation and Parameter Tuning
Working with Features
Introduction to Artificial Neural Networks and TensorFlow
Predictive Analytics with TensorFlow and Deep Neural Networks
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