3. Pixel classification

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    After enlightening data, we will now present the results of the classification algorithms applied on the “pixel” dataset, comparing methods, explaining results and choose best model with the best parameters for this dataset. The python pipeline used to apply cross validation on these models is available here.

• 3.1. Tree Based Classifiers
  • 3.1.1. Random forest
  • 3.1.2. Extreme Gradient Boosting
  • 3.1.3. Balancing dataset : Oversampling and Undersampling
• 3.2. Neighbourhood based classifiers
  • 3.2.1. K-Nearest Neighbours
  • 3.2.2. K-means
  • 3.2.3. Support Vector Machine
• 3.3. Pixels classification conclusion