This is a brief section about multiclass classification. This is the situation when we have more than two classes in our target. Models such as classification trees can handle this case using basically the same logic that we explained. For other models such as logistic regression, which is defined only for two classes, the most common approach is called One-vs-the-Rest or One-versus-All. This strategy can only be used with models that produce probabilities or other scores that can be interpreted as confidence of the classification. This method consists of fitting one classifier per class (that class versus the rest); the observations in that class will be considered the positive class and the rest the negative class. After all models have been trained, the class that is assigned to the observations is that of the model that produced the highest probability...
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Book Overview & Buying
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Table Of Contents
Hands-On Predictive Analytics with Python
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Hands-On Predictive Analytics with Python
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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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