KNN regressor #28
Merged
KNN regressor #28
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
|
Merged.Thanks! |
Hi @sagnik1511 can you please add the jwoc as well as the difficulty tags in the PR too. |
sagnik1511
added
easy
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Outdated suggestions cannot be applied.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.
PR fix for issue #6
Added KNN regressor to models.py the advantage of this is it takes almost zero time to train because it only stores the data of the training part. and faster than all the models mentioned in the models.py file in training time as its zero in KNN regressor , it is also a non parametric models with only parameter that needs to be mentioned is the number of neighbours, adding to this as KNN doesn't undergo training we can add new data to it which doesn't affect the accuracy of the model. It is also very easy to implement and interpret as there is only one hyperparameter which is the number of neighbours, apart from this it's versatile and can be used as a regressor as well as classifier