Machine Learning with Python Cookbook by Chris Albon

Author:Chris Albon
Language: eng
Format: epub, pdf
Publisher: O'Reilly Media, Inc.
Published: 2018-03-16T04:00:00+00:00

Discussion

Confusion matrices are an easy, effective visualization of a classifier’s performance. One of the major benefits of confusion matrices is their interpretability. Each column of the matrix (often visualized as a heatmap) represents predicted classes, while every row shows true classes. The end result is that every cell is one possible combination of predict and true classes. This is probably best explained using an example. In the solution, the top-left cell is the number of observations predicted to be Iris setosa (indicated by the column) that are actually Iris setosa (indicated by the row). This means the models accurately predicted all Iris setosa flowers. However, the model does not do as well at predicting Iris virginica. The bottom-right cell indicates that the model successfully predicted nine observations were Iris virginica, but (looking one cell up) predicted six flowers to be viriginica that were actually Iris versicolor.

There are three things worth noting about confusion matrices. First, a perfect model will have values along the diagonal and zeros everywhere else. A bad model will look like the observation counts will be spread evenly around cells. Second, a confusion matrix lets us see not only where the model was wrong, but also how it was wrong. That is, we can look at patterns of misclassification. For example, our model had an easy time differentiating Iris virginica and Iris setosa, but a much more difficult time classifying Iris virginica and Iris versicolor. Finally, confusion matrices work with any number of classes (although if we had one million classes in our target vector, the confusion matrix visualization might be difficult to read).

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