- Alejandro Leonardo García Navarro - alexgaarciia
- Lucía Cordero Sánchez - lucia-corsan
- Simon Dunand - SquintyG33Rs
- https://github.com/alexgaarciia/ExperimentsMachineLearning
- Language used; Python
- Done in Pycharm, but works for any other Python IDE
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Datasets: Penguins and Abalone datasets.
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Main tasks:
- Load datasets and divide into train and test, see if balanced.
- Train and test classifiers:
- Decision Tree with default parameters.
- Decision Tree with different choices of criterion, maximum depth and minimum samples split.
- Multilayered Perceptron with 2 hidden layers, SGD (Stochastic Gradient Descent) and sigmoid activation function.
- Previous MLP but experimenting with the activation functions, architecture and solver.
- Statistics of the previous classifiers: confusion matrix, recall, F1 (these three for the classes) and accuracy, macro-average/weighted-average/variation F1 (for the model).
- Analysis of performance.
- Files:
- main: Contains the solution to all the exercises proposed in the instructions.
- functions: Contains all the classifiers and functions to (a)compute some metrics, (b)print information, and (c)evaluate all the models.
- Instructions: run the "main" file to obtain all the plots and training/testing the classifiers.