In previous sections, you learned how to use L1 regularization to zero out irrelevant features via logistic regression, and use the SBS algorithm for feature selection and apply it to a KNN algorithm. Another useful approach to select relevant features from a dataset is to use a random forest, an ensemble technique that we introduced in Chapter 3, A Tour of Machine Learning Classifiers Using scikit-learn. Using a random forest, we can measure the feature importance as the averaged impurity decrease computed from all decision trees in the forest, without making any assumptions about whether our data is linearly separable or not. Conveniently, the random forest implementation in scikit-learn already collects the feature importance values for us so that we can access them via the feature_importances_
attribute after fitting a RandomForestClassifier
. By executing the following code, we will now train a forest of 500 trees on the Wine dataset and...
Python Machine Learning, Second Edition - Second Edition
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Python Machine Learning, Second Edition - Second Edition
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Overview of this book
Publisher's Note: This edition from 2017 is outdated and is not compatible with TensorFlow 2 or any of the most recent updates to Python libraries. A new third edition, updated for 2020 and featuring TensorFlow 2 and the latest in scikit-learn, reinforcement learning, and GANs, has now been published.
Machine learning is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka’s bestselling book, Python Machine Learning. Using Python's open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis.
Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow 1.x deep learning library. The scikit-learn code has also been fully updated to v0.18.1 to include improvements and additions to this versatile machine learning library.
Sebastian Raschka and Vahid Mirjalili’s unique insight and expertise introduce you to machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. By the end of the book, you’ll be ready to meet the new data analysis opportunities.
If you’ve read the first edition of this book, you’ll be delighted to find a balance of classical ideas and modern insights into machine learning. Every chapter has been critically updated, and there are new chapters on key technologies. You’ll be able to learn and work with TensorFlow 1.x more deeply than ever before, and get essential coverage of the Keras neural network library, along with updates to scikit-learn 0.18.1.
Table of Contents (24 chapters)
Python Machine Learning Second Edition
Credits
About the Authors
About the Reviewers
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Preface
Free Chapter
Giving Computers the Ability to Learn from Data
Training Simple Machine Learning Algorithms for Classification
A Tour of Machine Learning Classifiers Using scikit-learn
Building Good Training Sets – Data Preprocessing
Compressing Data via Dimensionality Reduction
Learning Best Practices for Model Evaluation and Hyperparameter Tuning
Combining Different Models for Ensemble Learning
Applying Machine Learning to Sentiment Analysis
Embedding a Machine Learning Model into a Web Application
Predicting Continuous Target Variables with Regression Analysis
Working with Unlabeled Data – Clustering Analysis
Implementing a Multilayer Artificial Neural Network from Scratch
Parallelizing Neural Network Training with TensorFlow
Going Deeper – The Mechanics of TensorFlow
Classifying Images with Deep Convolutional Neural Networks
Modeling Sequential Data Using Recurrent Neural Networks
Index
Customer Reviews