I need to classify a relatively small time series dataset.

Training set dimensions are 5087 rows (to classify) by 3197 columns (time samples) which are (or should be as far as I understood) the features of the model. I don't know yet if every sample is important and I will think about downsample/filtering/fourier transform later.

Unfortunately dataset is extremely unbalanced: only 37 (0.7%) out of 5087 rows are "Positive". How would you approach this? I will have to use Scikit-learn library.

Since this is my first approach with Scikit-learn I wanted to try a very simple classifier, with few hyperparameters,and build up from there.

First, choosing the classifier: logistic regression because is the easiest I can think of an this is just a test. Second, choosing regularization parameter via tuning grid Third, choosing the splitting cross validation strategy: I wanted to use stratified bootstrap but unfortunately it is not provided by the library so I opted for Stratified shuffle split Fourth, choosing the metric: cohen's kappa because the dataset is so unbalanced the make accuracy too much biased


classifier = LogisticRegression(tol=1e-4, max_iter=500, random_state=1)
param_grid = {'C': list(range(3))}
splitter = StratifiedShuffleSplit(n_splits=5, random_state=1)
grid_searcher = GridSearchCV(classifier, param_grid, cv=splitter, scoring=make_scorer(cohen_kappa_score))
model = grid_searcher.fit(train_x, train_y)

First is "cv=splitter" legit? Second, what do you think of this approach? Obviously with such a trivial classifier the model predicted all Negative and I also got some warnings:

FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan ZeroDivisionError: float division by zero


1 Answer 1


Your dataset is extremely unbalanced, and most of the models would just ignore these 37 samples. After all, failing 0.7% of any test seems to be an extremely good result!

There are several ways to address the imbalanced dataset. I suggest two options: (1) Assign a very high penalty on misclassification of positive samples -- your loss function would be weighted L2, (2) Resampling -- when you draw a random row, assign a higher probability to get the positive sample than negative.

See, for example, https://www.kaggle.com/rafjaa/resampling-strategies-for-imbalanced-datasets for implementation. And How to deal with class imbalance in a neural network?


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.