As we discussed when talking about trees, one of their advantages is their simplicity; but that is also the cause of their problems—their performance is often worse than other models, especially if the tree is small, in which case we will have what is known as a weak predictor. By the end of the 1980s, two researchers, Kearns and Valiant (1988, 1989), posted the question "Can a set of weak learners create a single strong learner?" This question gave rise to a lot of research on what is known as Ensemble methods or Ensemble Learning. The core idea of Ensemble Learning is simple—instead of using just one model to make predictions, use many individual models and combine their predictions. This simple idea has been one of the keys in the success of machine learning in producing very accurate models. Ensemble Learning is, of course, a whole sub...
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Book Overview & Buying
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Table Of Contents
Hands-On Predictive Analytics with Python
By :
Hands-On Predictive Analytics with Python
By:
Overview of this book
Predictive analytics is an applied field that employs a variety of quantitative methods using data to make predictions. It involves much more than just throwing data onto a computer to build a model. This book provides practical coverage to help you understand the most important concepts of predictive analytics. Using practical, step-by-step examples, we build predictive analytics solutions while using cutting-edge Python tools and packages.
The book's step-by-step approach starts by defining the problem and moves on to identifying relevant data. We will also be performing data preparation, exploring and visualizing relationships, building models, tuning, evaluating, and deploying model.
Each stage has relevant practical examples and efficient Python code. You will work with models such as KNN, Random Forests, and neural networks using the most important libraries in Python's data science stack: NumPy, Pandas, Matplotlib, Seaborn, Keras, Dash, and so on. In addition to hands-on code examples, you will find intuitive explanations of the inner workings of the main techniques and algorithms used in predictive analytics.
By the end of this book, you will be all set to build high-performance predictive analytics solutions using Python programming.
Table of Contents (11 chapters)
Preface
Free Chapter
The Predictive Analytics Process
Problem Understanding and Data Preparation
Dataset Understanding – Exploratory Data Analysis
Predicting Numerical Values with Machine Learning
Predicting Categories with Machine Learning
Introducing Neural Nets for Predictive Analytics
Model Evaluation
Model Tuning and Improving Performance
Implementing a Model with Dash
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