Text Classification using Spark Machine Learning
The goal of text classification is the classification of text documents into a fixed number of predefined categories. Text classification has a number of applications ranging from email spam detection to providing news feed content to users based on user preferences.
In this session, we explore how to perform text classification using Spark’s Machine Learning Library (MLlib). We see how MLlib provides a set of high-level APIs for constructing, evaluating and tuning a machine learning workflow. We explore how Spark represents a workflow as a Pipeline, which consists of a sequence of stages to be run in a specific order. The Pipeline for our text classification use case utilizes Transformer stages to prepare the raw text documents for classification, and Estimator stages to learn a machine learning model that can be used to classify documents. Finally, we illustrate how to tune the model for best fit.
Although a document classification use case is specifically explored, many of the principles demonstrated in the session can be employed in a variety of other machine learning use cases.
Here's the link to the slides
https://ibm.box.com/s/atp4ezwvo5jr27z...
And the link to the notebook as an .ipynb file.
https://ibm.box.com/s/spcj7f3uz6qetq8...
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