更新时间：2021-07-02 14:23:35
coverpage
Title Page
Copyright and Credits
R Machine Learning Projects
About Packt
Why subscribe?
Packt.com
Dedication
Contributors
About the author
About the reviewers
Packt is searching for authors like you
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Reviews
Exploring the Machine Learning Landscape
ML versus software engineering
Types of ML methods
Supervised learning
Unsupervised learning
Semi-supervised learning
Reinforcement learning
Transfer learning
ML terminology – a quick review
Deep learning
Big data
Natural language processing
Computer vision
Cost function
Model accuracy
Confusion matrix
Predictor variables
Response variable
Dimensionality reduction
Class imbalance problem
Model bias and variance
Underfitting and overfitting
Data preprocessing
Holdout sample
Hyperparameter tuning
Performance metrics
Feature engineering
Model interpretability
ML project pipeline
Business understanding
Understanding and sourcing the data
Preparing the data
Model building and evaluation
Model deployment
Learning paradigm
Datasets
Summary
Predicting Employee Attrition Using Ensemble Models
Philosophy behind ensembling
Getting started
Understanding the attrition problem and the dataset
K-nearest neighbors model for benchmarking the performance
Bagging
Bagged classification and regression trees (treeBag) implementation
Support vector machine bagging (SVMBag) implementation
Naive Bayes (nbBag) bagging implementation
Randomization with random forests
Implementing an attrition prediction model with random forests
Boosting
The GBM implementation
Building attrition prediction model with XGBoost
Stacking
Building attrition prediction model with stacking
Implementing a Jokes Recommendation Engine
Fundamental aspects of recommendation engines
Recommendation engine categories
Content-based filtering
Collaborative filtering
Hybrid filtering
Understanding the Jokes recommendation problem and the dataset
Converting the DataFrame
Dividing the DataFrame
Building a recommendation system with an item-based collaborative filtering technique
Building a recommendation system with a user-based collaborative filtering technique
Building a recommendation system based on an association-rule mining technique
The Apriori algorithm
Content-based recommendation engine
Differentiating between ITCF and content-based recommendations
Building a hybrid recommendation system for Jokes recommendations
References
Sentiment Analysis of Amazon Reviews with NLP
The sentiment analysis problem
Understanding the Amazon reviews dataset
Building a text sentiment classifier with the BoW approach
Pros and cons of the BoW approach
