Nejc Ilenic

Optimize performance of CSVLoader

4

The current implementation is very slow, I think a better approach would be to implement a custom solution rather than using a third-party library.

Currently, the processing is sequential.

Currently, `Pipeline` only supports the `predict` function but should also expose the `predictProba` if the predictor is a classifier.

When the best performing model is returned from grid/random search and it is evaluated on the test set, a user might want to retrain it on a whole dataset with...

Currently, we throw errors at runtime if `.predict` is called on an unfitted estimator, conversely, we throw an error if `.fit` is called on a trained estimator. The idea of...

**Describe the solution you'd like** [CrossValidation](https://github.com/picnicml/doddle-model/blob/master/src/main/scala/io/picnicml/doddlemodel/modelselection/CrossValidation.scala#L22) should receive and calculate an arbitrary number of metrics specified by the user.

Set up https://scalameta.org/scalafmt/. From their website: > Spend more time discussing important issues in code review and less time on code style. Scalafmt formats code so that it looks consistent...

Create and publish Scaladoc documentation

1

Generate documentation based on Scaladoc and publish it on http://picnicml.github.io.

Identify which algorithms would be the most useful to implement first. For a list of existing implementations take a look at the [examples repository](https://github.com/picnicml/doddle-model-examples), for a list of scikit-learn implementations...

Return all scores from cross validation

2

Currently, the average score from each fold is computed and returned when calling `crossVal.score(...)`. We are often interested in computing the mean and standard deviation, however, so all scores (or...