Uploaded June 2019 | Updated September 2026, 2 weeks ago

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Explaining how to perform Digits Classification using Random Forest model in Machine Learning just in 10 minutes. The whole example is coded in Python programming language.

Used data: standard handwritten digits from sklearn module: scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html

You will find how to:
- Visualize digits by using Matplotlib in a subplot.
- Generate Train and Test sets for Machine Learning model.
- Build a simple Random Forest model with estimators.
- Make predictions for a class in Random Forest.
- Generate Classification Reports (thanks to sklearn)
- Build a Confusion Matrix and Visualize it on Heatmap based on Seaborn.

Some definitions to be more familiar with model evaluation are below:

Precision.
What proportion of positive identifications was actually correct?

Recall
What proportion of actual positives was identified correctly?

F1-score.
The F1 score is the harmonic average of the precision and recall,
where an F1 score reaches its best value at 1 (perfect precision
and recall) and worst at 0.

Support.
The support is the number of samples of the true response that lie in that class.

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