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According to wikipedia, the definition regularization is the process of adding information in order to solve an ill-posed problem or to prevent overfitting. One common approach is to add a penalty term for large parameter values to the loss function. There are many other approaches to regularization. Here are a couple of other examples: Increasing the ...


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The answer is in the question :) Only human eyes can judge of the readable character of a text, its creativity, its grammatical correctness etc. In the example of a model trained on Shakespeare's writing, take a group of human annotators (preferably literature experts) and ask them to annotate texts as likely authored by Shakespeare or not (variant: mark ...


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"Predict how many points a player can be expected to have at the end of the season" could be framed as count-based regression problem (not time series). A player will have a collect of features (e.g., age, position, team, minutes plays). A machine learning model will learn how to weight to the features to make a prediction. In this case, the ...


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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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The general algorithms would be Deep Reinforcement Learning. One possible approach would be to collect a large number of games. Then train the agent on those games. Another possible approach would be the agent play against itself, aka self play.


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This looks like a sequence labelling problem, the most common such problem in NLP being Named Entity Recognition (NER). You'll find a lot of libraries and tutorials about NER. It can be done with Conditional Random Fields but there are also neural methods nowadays. Assuming your problem is not about standard entities (like persons names, organizations, ...


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The other reason for failing to generalize would be that the model is over-fitting and has learned how to identify the individuals in the training data. If including new data into your training set doesn't improve your results, try removing parameters from the model or drop out a layer and see how it affects the test results.


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Your model is failing to generalize to new data. One possible reason is the new data is not like the training data. There could be many reasons: Different speakers Different audio recording process Different audio preprocessing Different file format conversions Different audio post processing The deep learning model over-learned the specific training ...


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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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