This work shows how random forests can be used to learn a model to classify and predict handwritten digits. First, the 28x28 greyscale images of handwritten digits are imported as a vector of length 784 with values between 0 and 255. The training data also contains their labels (the number it displays). Then, the random forest classifier is learnt and applied to the test data.

The data set is taken from the respective kaggle.com challenge, and you can find my kernel here: https://www.kaggle.com/michaelnie/simple-random-forest-classification/

The employed classifier achieved an accuracy of nearly 94% which is quite remarkable considering its simplicity.

I hope you enjoy the introduction to handwritten digit classification with random forests. If you have any questions, please let me know!