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  ee.Classifier.confusionMatrix()  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

Last modified: Saturday, 10 July 2021, 2:04 PM