Hands-On Ensemble Learning with Python Build Highly Optimized Ensemble Machine Learning Models Using Scikit-Learn and Keras
Ensemble learning can provide the necessary methods to improve the accuracy and performance of existing models. In this book, you'll understand how to combine different machine learning algorithms to produce more accurate results from your models.
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| Main Authors | , |
|---|---|
| Format | eBook |
| Language | English |
| Published |
Birmingham
Packt Publishing, Limited
2019
Packt Publishing Limited Packt Publishing |
| Edition | 1 |
| Subjects | |
| Online Access | Get full text |
| ISBN | 1789612853 9781789612851 |
| DOI | 10.0000/9781789617887 |
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| Abstract | Ensemble learning can provide the necessary methods to improve the accuracy and performance of existing models. In this book, you'll understand how to combine different machine learning algorithms to produce more accurate results from your models. |
|---|---|
| AbstractList | Combine popular machine learning techniques to create ensemble models using PythonKey FeaturesImplement ensemble models using algorithms such as random forests and AdaBoostApply boosting, bagging, and stacking ensemble methods to improve the prediction accuracy of your model Explore real-world data sets and practical examples coded in scikit-learn and KerasBook DescriptionEnsembling is a technique of combining two or more similar or dissimilar machine learning algorithms to create a model that delivers superior predictive power. This book will demonstrate how you can use a variety of weak algorithms to make a strong predictive model.With its hands-on approach, you'll not only get up to speed on the basic theory but also the application of various ensemble learning techniques. Using examples and real-world datasets, you'll be able to produce better machine learning models to solve supervised learning problems such as classification and regression. Furthermore, you'll go on to leverage ensemble learning techniques such as clustering to produce unsupervised machine learning models. As you progress, the chapters will cover different machine learning algorithms that are widely used in the practical world to make predictions and classifications. You'll even get to grips with the use of Python libraries such as scikit-learn and Keras for implementing different ensemble models.By the end of this book, you will be well-versed in ensemble learning, and have the skills you need to understand which ensemble method is required for which problem, and successfully implement them in real-world scenarios.What you will learnImplement ensemble methods to generate models with high accuracyOvercome challenges such as bias and varianceExplore machine learning algorithms to evaluate model performanceUnderstand how to construct, evaluate, and apply ensemble modelsAnalyze tweets in real time using Twitter's streaming APIUse Keras to build an ensemble of neural networks for the MovieLens datasetWho this book is forThis book is for data analysts, data scientists, machine learning engineers and other professionals who are looking to generate advanced models using ensemble techniques. An understanding of Python code and basic knowledge of statistics is required to make the most out of this book. Ensemble learning can provide the necessary methods to improve the accuracy and performance of existing models. In this book, you'll understand how to combine different machine learning algorithms to produce more accurate results from your models. |
| Author | Margaritis, Konstantinos G Kyriakides, George |
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| Snippet | Ensemble learning can provide the necessary methods to improve the accuracy and performance of existing models. In this book, you'll understand how to combine... Combine popular machine learning techniques to create ensemble models using PythonKey FeaturesImplement ensemble models using algorithms such as random forests... |
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| SubjectTerms | COM016000 COMPUTERS / Computer Vision & Pattern Recognition COMPUTERS / Data Modeling & Design COMPUTERS / Information Technology Machine learning Python (Computer program language) |
| Subtitle | Build Highly Optimized Ensemble Machine Learning Models Using Scikit-Learn and Keras |
| TableOfContents | Cover -- Title Page -- Copyright and Credits -- About Packt -- Contributors -- Table of Contents -- Preface -- Section 1: Introduction and Required Software Tools -- Chapter 1: A Machine Learning Refresher -- Technical requirements -- Learning from data -- Popular machine learning datasets -- Diabetes -- Breast cancer -- Handwritten digits -- Supervised and unsupervised learning -- Supervised learning -- Unsupervised learning -- Dimensionality reduction -- Performance measures -- Cost functions -- Mean absolute error -- Mean squared error -- Cross entropy loss -- Metrics -- Classification accuracy -- Confusion matrix -- Sensitivity, specificity, and area under the curve -- Precision, recall, and the F1 score -- Evaluating models -- Machine learning algorithms -- Python packages -- Supervised learning algorithms -- Regression -- Support vector machines -- Neural networks -- Decision trees -- K-Nearest Neighbors -- K-means -- Summary -- Chapter 2: Getting Started with Ensemble Learning -- Technical requirements -- Bias, variance, and the trade-off -- What is bias? -- What is variance? -- Trade-off -- Ensemble learning -- Motivation -- Identifying bias and variance -- Validation curves -- Learning curves -- Ensemble methods -- Difficulties in ensemble learning -- Weak or noisy data -- Understanding interpretability -- Computational cost -- Choosing the right models -- Summary -- Section 2: Non-Generative Methods -- Chapter 3: Voting -- Technical requirements -- Hard and soft voting -- Hard voting -- Soft voting -- Python implementation -- Custom hard voting implementation -- Analyzing our results using Python -- Using scikit-learn -- Hard voting implementation -- Soft voting implementation -- Analyzing our results -- Summary -- Chapter 4: Stacking -- Technical requirements -- Meta-learning -- Stacking -- Creating metadata Deciding on an ensemble's composition -- Selecting base learners -- Selecting the meta-learner -- Python implementation -- Stacking for regression -- Stacking for classification -- Creating a stacking regressor class for scikit-learn -- Summary -- Section 3: Generative Methods -- Chapter 5: Bagging -- Technical requirements -- Bootstrapping -- Creating bootstrap samples -- Bagging -- Creating base learners -- Strengths and weaknesses -- Python implementation -- Implementation -- Parallelizing the implementation -- Using scikit-learn -- Bagging for classification -- Bagging for regression -- Summary -- Chapter 6: Boosting -- Technical requirements -- AdaBoost -- Weighted sampling -- Creating the ensemble -- Implementing AdaBoost in Python -- Strengths and weaknesses -- Gradient boosting -- Creating the ensemble -- Further reading -- Implementing gradient boosting in Python -- Using scikit-learn -- Using AdaBoost -- Using gradient boosting -- XGBoost -- Using XGBoost for regression -- Using XGBoost for classification -- Other boosting libraries -- Summary -- Chapter 7: Random Forests -- Technical requirements -- Understanding random forest trees -- Building trees -- Illustrative example -- Extra trees -- Creating forests -- Analyzing forests -- Strengths and weaknesses -- Using scikit-learn -- Random forests for classification -- Random forests for regression -- Extra trees for classification -- Extra trees regression -- Summary -- Section 4: Clustering -- Chapter 8: Clustering -- Technical requirements -- Consensus clustering -- Hierarchical clustering -- K-means clustering -- Strengths and weaknesses -- Using scikit-learn -- Using voting -- Using OpenEnsembles -- Using graph closure and co-occurrence linkage -- Graph closure -- Co-occurrence matrix linkage -- Summary -- Section 5: Real World Applications Chapter 9: Classifying Fraudulent Transactions -- Technical requirements -- Getting familiar with the dataset -- Exploratory analysis -- Evaluation methods -- Voting -- Testing the base learners -- Optimizing the decision tree -- Creating the ensemble -- Stacking -- Bagging -- Boosting -- XGBoost -- Using random forests -- Comparative analysis of ensembles -- Summary -- Chapter 10: Predicting Bitcoin Prices -- Technical requirements -- Time series data -- Bitcoin data analysis -- Establishing a baseline -- The simulator -- Voting -- Improving voting -- Stacking -- Improving stacking -- Bagging -- Improving bagging -- Boosting -- Improving boosting -- Random forests -- Improving random forest -- Summary -- Chapter 11: Evaluating Sentiment on Twitter -- Technical requirements -- Sentiment analysis tools -- Stemming -- Getting Twitter data -- Creating a model -- Classifying tweets in real time -- Summary -- Chapter 12: Recommending Movies with Keras -- Technical requirements -- Demystifying recommendation systems -- Neural recommendation systems -- Using Keras for movie recommendations -- Creating the dot model -- Creating the dense model -- Creating a stacking ensemble -- Summary -- Chapter 13: Clustering World Happiness -- Technical requirements -- Understanding the World Happiness Report -- Creating the ensemble -- Gaining insights -- Summary -- Another Book You May Enjoy -- Index Hands-On Ensemble Learning with Python: Build highly optimized ensemble machine learning models using scikit-learn and Keras |
| Title | Hands-On Ensemble Learning with Python |
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