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Summary

This chapter starts with basic definitions such as types of machine learning (supervised vs. unsupervised learning, classifiers vs. regressors), types of features (binary, categorical, discrete, continuos), metrics (precision, recall, f-measure, accuracy, overfitting), and raw data and then defines the machine learning cycle and the feature engineering cycle. The feature engineering cycle hinges on two types of analysis: exploratory data analysis, at the beginning of the cycle and error analysis at the end of each feature engineering cycle. Domain modelling and feature construction concludes the chapter with particular emphasis on feature ideation techniques.

When machine learning engineers work with data sets, they may find the results aren't as good as they need. Instead of improving the model or collecting more data, they can use the feature engineering process to help improve results by modifying the data's features to better capture the nature of the problem. This practical guide to feature engineering is an essential addition to any data scientist's or machine learning engineer's toolbox, providing new ideas on how to improve the performance of a machine learning solution. Beginning with the basic concepts and techniques, the text builds up to a unique cross-domain approach that spans data on graphs, texts, time series, and images, with fully worked out case studies. Key topics include binning, out-of-fold estimation, feature selection, dimensionality reduction, and encoding variable-length data. The full source code for the case studies is available on a companion website as Python Jupyter notebooks.

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1 - Introduction

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The Art of Feature Engineering

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2 results

1 - Introduction

Summary

The Art of Feature Engineering