Supervised learning – classification and regression

In this section, we will show short examples for both classification and regression.

Classification problems are pervasive: document categorization, fraud detection, market segmentation in business intelligence, and protein function prediction in bioinformatics.

While it might be possible for hand-craft rules to assign a category or label to new data, it is faster to use algorithms to learn and generalize from the existing data.

We will continue with the Iris dataset. Before we apply a learning algorithm, we want to get an intuition of the data by looking at some values and plots.

All measurements share the same dimension, which helps to visualize the variance in various boxplots:

We see that the ...

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