Database Reference
In-Depth Information
Types of machine learning models
While we have highlighted a few use cases for machine learning in the context of the pre-
ceding MovieStream example, there are many other examples, some of which we will
touch on in the relevant chapters when we introduce each machine learning task.
However, we can broadly divide the preceding use cases and methods into two categories
of machine learning:
Supervised learning : These types of models use labeled data to learn. Recom-
mendation engines, regression, and classification are examples of supervised learn-
ing methods. The labels in these models can be user-movie ratings (for recom-
mendation), movie tags (in the case of the preceding classification example), or
revenue figures (for regression). We will cover supervised learning models in
Chapter 4 , Building a Recommendation Engine with Spark , Chapter 5 , Building a
Classification Model with Spark , and Chapter 6 , Building a Regression Model with
Spark .
Unsupervised learning : When a model does not require labeled data, we refer to
unsupervised learning. These types of models try to learn or extract some underly-
ing structure in the data or reduce the data down to its most important features.
Clustering, dimensionality reduction, and some forms of feature extraction, such as
text processing, are all unsupervised techniques and will be dealt with in Chapter
7 , Building a Clustering Model with Spark , Chapter 8 , Dimensionality Reduction
with Spark , and Chapter 9 , Advanced Text Processing with Spark .
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