Book Image

Mastering Predictive Analytics with scikit-learn and TensorFlow

By : Alvaro Fuentes
Book Image

Mastering Predictive Analytics with scikit-learn and TensorFlow

By: Alvaro Fuentes

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)

Ensemble Methods for Regression and Classification

Advanced analytical tools are widely used by business enterprises in order to solve problems using data. The goal of analytical tools is to analyze data and extract relevant information that can be used to solve problems or increase performance of some aspect of the business. It also involves various machine learning algorithms with which we can create predictive models for better results.

In this chapter, we are going to explore a simple idea that can drastically improve the performance of basic predictive models.

We are going to cover the following topics in this chapter:

  • Ensemble methods and their working
  • Ensemble methods for regression
  • Ensemble methods for classification