The classifier model that we will be generating in this approach should give us the best possible accuracy. We have already discussed this approach. If you are new to ensemble ML models, then let me give you a basic intuitive idea behind it. In layman's terms, ensemble ML models basically use a combination of various ML algorithms. What is the benefit of combining various ML models together? Well, we know there is no single classifier that can perfectly classify all the samples, so if we combine more than one classifier, then we can get more accuracy because the problem with one classifier can be overcome by another classifier. Due to this reason, we will use a voting classifier that is a type of ensemble classifier.
Machine Learning Solutions
Machine Learning Solutions
Overview of this book
Machine learning (ML) helps you find hidden insights from your data without the need for explicit programming. This book is your key to solving any kind of ML problem you might come across in your job.
You’ll encounter a set of simple to complex problems while building ML models, and you'll not only resolve these problems, but you’ll also learn how to build projects based on each problem, with a practical approach and easy-to-follow examples.
The book includes a wide range of applications: from analytics and NLP, to computer vision domains. Some of the applications you will be working on include stock price prediction, a recommendation engine, building a chat-bot, a facial expression recognition system, and many more. The problem examples we cover include identifying the right algorithm for your dataset and use cases, creating and labeling datasets, getting enough clean data to carry out processing, identifying outliers, overftting datasets, hyperparameter tuning, and more. Here, you'll also learn to make more timely and accurate predictions.
In addition, you'll deal with more advanced use cases, such as building a gaming bot, building an extractive summarization tool for medical documents, and you'll also tackle the problems faced while building an ML model. By the end of this book, you'll be able to fine-tune your models as per your needs to deliver maximum productivity.
Table of Contents (19 chapters)
Machine Learning Solutions
Foreword
Contributors
Preface
Free Chapter
Credit Risk Modeling
Stock Market Price Prediction
Customer Analytics
Recommendation Systems for E-Commerce
Sentiment Analysis
Job Recommendation Engine
Text Summarization
Developing Chatbots
Building a Real-Time Object Recognition App
Face Recognition and Face Emotion Recognition
Building Gaming Bot
List of Cheat Sheets
Strategy for Wining Hackathons
Index
Customer Reviews