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A proof of concept for a ML model is the same as in the ML research literature: Design or adopt a suitable evaluation method specifically for the task. Prove that the evaluation design is appropriate, including explanation about any data collection, preprocessing, etc. Evaluate performance in a reliable and accurate way. Prove that the performance value ...


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Since you partly overfit with RF, first try to get the RF hyperparameter right. You could do a grid search like: rf = RandomForestClassifier(...) param_grid = { 'n_estimators': [200,300], 'max_features': [10,20,30] } cv = GridSearchCV(estimator=rf, param_grid=param_grid, cv= 5) cv.fit(xtrain, ytrain) In RandomForestClassifier max_depth and ...


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I think you have an issue with the number of elements in your dataset for analyzing a neural network with 257 features. Consider reducing the number of features. Are all of them mandatory? What is the correlation between them? What is the mutual information between all these variables? Consider adding more data to you dataset. Is that possible? Could you add ...


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