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Tutorial 6: Step 11 Display a Classification Plot


Display a Classification Plot

1. If the Predictions item (or whatever you named it) in the Experiments navigator is not already highlighted, click it.

2. Select Classification Plot from the Predict menu, or right-click the item and select Classification Plot from the shortcut menu. The Classification Plot is displayed showing the predicted classes, the raw votes of the component classifiers and other information.

3. From the Comparison Variable drop-down list box in the upper right corner, select test classes. Some of the rectangles in the view turn red, signifying misclassifications.


This is a very rich display, and it may take some experience before you are able to interpret it easily.

Each row represents a sample. On the left of each row is a Sample name and Prediction or predicted class. The rest of the display consists of boxes representing the outputs of the artificial neural networks for each of the possible classes for that sample.

Each column represents a class. The colors of the boxes are significant:

Hence the number of red boxes in the display indicates the number of misclassifications. Reducing the rate of misclassifications is discussed below.


Component Classifier Votes

Inside each box is a representation of the votes of each of the neural networks in the committee. Each of 10 neural networks was trained on a different 90% of the training data. Each of the horizontal rectangles in the view above represents the output of all 10 neural networks for a given class on a given sample. If all 10 neural networks are in agreement (i.e. have the same output value) then there will be a solid bar - at the right end if they all have high output (i.e. that is the sample's class), at the left end if they all have low output (i.e. that is not the sample's class).


Class Prediction Process

The class prediction (or call) is done by a simple vote. For a given sample, each neural network votes for the class with the highest output. If 2/3 (default setting) of the networks agree on a single class, we call that a prediction. In any other case, no prediction is made and the sample is labelled 'Unknown'.



Reasons For Misclassifications:

There are often no misclassifications in the training data – artificial neural networks are fairly powerful and adaptable learners. If there are misclassifications, however, it may be for one of several possible reasons:  

The above reasons may affect either training or test results. If the training results are excellent but the test results are poor, it may be for one of the following additional reasons:

These last three conditions correspond to a condition called ‘overtraining’. You can think of this as analogous to a child learning a certain set of examples by rote, but failing to be able to generalize from the examples to new cases. When a neural network is either given too much memory for detail (too many hidden nodes or input nodes) or is forced to learn the input examples too well (stopping criteria too tight), then it may simply ‘memorize’ the training data to the detriment of generalizing well on test data.