July 2017
Intermediate to advanced
254 pages
6h 29m
English
Content preview from Mastering Machine Learning with scikit-learn - Second Edition
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Exploring the data
The training data contains the following explanatory variables: fixed acidity, volatile acidity, citric acid, residual sugar, chlorides, free sulfur dioxide, total sulfur dioxide, density, pH, sulphates, and alcohol content. Understanding these attributes could provide some insight as to the design of the model, and domain expertise is often important to designing successful machine learning systems. For this example, it is not necessary to be able to interpret the effects of the physicochemical attributes on the quality of wine, and the explanatory variables' units will be omitted for brevity. Let's examine a sample of the training data:
|
Fixed acidity |
Volatile acidity |
Citric acid |
Residual sugar |
Chlorides |
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