It is important to get a quantitative estimate of the accuracy of the classification. To do this, a common strategy is to divide your training samples into 2 random fractions - one used for
training the model and the other for validation
of the predictions. Once a classifier is trained, it can be used to classify the entire image. We can then compare the classified values with the ones in the validation fraction. We can use the
method to calculate a Confusion Matrix representing expected accuracy.
Don’t get carried away tweaking your model to give you the highest validation accuracy. You must use both qualitative measures (such as visual inspection of results) along with quantitative measures to assess the results.
Watch this video:
You can find the code of this exercise in the repository under Module5/Exercise2