Feat/regression #231
Open
Feat/regression #231
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
…e contains the parameters of RandomForestRegression
…t Boosting Regressor model.
… in the __init__.py from models
…g Regression parameters
…g the models for the regression metrics, in addition to the imports in the init
…methods implemented. And 2 child classes are added in the files sklearn_like_classifier.py and sklearn_like_regressor.py where each implements predict()
- Update MLPRegressor to MLPRegression in containers.py and models_schemas/MLPRegression.json - Update imports for MLPRegression in containers.py and models_schemas/MLPRegression.json - Remove unused parameters from models_schemas/MLPRegression.json
Gradient Boosting Regressor
Random Forest Regressor
MLPRegressor
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Outdated suggestions cannot be applied.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.
Summary
This pull request introduces new regression models and metrics.
New Regression Models and Metrics:
GradientBoostingR,LinearRegression,LinearSVR,MLPRegression,RandomForestRegression,RidgeRegression) and metrics (MAE,RMSE).