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Applied Supervised Learning with Python

You're reading from   Applied Supervised Learning with Python Use scikit-learn to build predictive models from real-world datasets and prepare yourself for the future of machine learning

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Product type Paperback
Published in Apr 2019
Publisher
ISBN-13 9781789954920
Length 404 pages
Edition 1st Edition
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Authors (2):
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Ishita Mathur Ishita Mathur
Author Profile Icon Ishita Mathur
Ishita Mathur
Benjamin Johnston Benjamin Johnston
Author Profile Icon Benjamin Johnston
Benjamin Johnston
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Regression and Classification Problems


We discussed two distinct methods, supervised learning and unsupervised learning, in Chapter 1, Python Machine Learning Toolkit. Supervised learning problems aim to map input information to a known output value or label, but there are two further subcategories to consider. Both supervised and unsupervised learning problems can be further divided into regression or classification problems. Regression problems, which are the subject of this chapter, aim to predict or model continuous values, for example, predicting the temperature tomorrow in degrees Celsius or determining the location of a face within an image. In contrast, classification problems, rather than returning a continuous value, predict membership of one of a specified number of classes or categories. The example supervised learning problem in Chapter 1, Python Machine Learning Toolkit, where we wanted to determine or predict whether a wig was from the 1960s or 1980s, is a good example of...

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