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How can I explain this drop in performance on test data?
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I am asking the question here, despite the fact that I hesitated to post it on CrossValidated (or DataScience) StackExchange. i've a dataset of 60 classified objects (for use for education) and 150 unlabeled gadgets (for take a look at). The goal of the hassle is to expect the labels of the 150 gadgets (this used to receive as a homework problem). For every object, I computed 258 capabilities. considering each item as a sample, i've X_train : (60,258), y_train : (60,) (labels of the items used for training) and X_test : (one hundred fifty,258). due to the fact the solution of the homework hassle was given, I additionally have the proper labels of the a hundred and fifty objects, in y_test : (a hundred and fifty,).

A good way to are expecting the labels of the a hundred and fifty items, I pick out to apply a LogisticRegression (the Scikit-research implementation). The classifier is educated on (X_train, y_train), after the facts has been normalized, and used to make predictions for the a hundred and fifty objects. the ones predictions are compared to y_test to assess the performance of the model. For reproducibility, I replica the code i have used. https://goo.gl/7RTVvJ
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