In this report we train different models: Multi-Layer Perceptron (MLP), Random Forest (RF) and Support Vector Machine (SVM) on the given dataset. We’ve analysed performance and accuracy after choosing the best parameters for each model implementation by using grid searches and k-fold cross validation. According to our results, the best model is the one obtained with Extremely Randomized Trees.
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Machine Learning Project for the UNIPI 2019 MLCUP
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