Introduce k-nearest neighbors estimators #38
Merged
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
This PR introduces support for k-nearest neighbors classification and regression, and a number of different distance metrics. Some distance metrics were only supported by either scikit-learn (e.g., mahalanobis) or PMML (e.g., squaredEuclidean), and hence were left out for now. Since the implementation by scikit-learn allows for callable metric functions, support could be added in future.
Since distance metrics generally only work on either numerical or categorical, but not mixed column types, "categorical support" proved a bit challenging. Some work is being done to address this, but until then I chose to leave out categorical support. In addition, I think one-hot encoding is a bad approach and will attribute more weight to categorical columns.