Ensemble Methods
Adaboost, Bagging, ExtraTrees, GradientBoosting, GradientBoosting, Isolation Forest, Random Forest, Stacking, Voting, HistGradientBoosting
Last updated
Adaboost, Bagging, ExtraTrees, GradientBoosting, GradientBoosting, Isolation Forest, Random Forest, Stacking, Voting, HistGradientBoosting
Last updated
For both Classifier & Regressor
Inputs
*Target Data(X)
Data Input
*Model Name
Name of the user-defined model
Predict Output
Name of the output data
Merge Data
Predict Output will be created with predicted labels concatenated to Merge Data.
*X
User-selected columns to be X.
*Y
User-selected columns to be Y.
In case of using AdaBoost and Bagging models, you have to define the base_estimator in Arguments.
For both Classifier & Regressor
Inputs
*Target Data
Data Input
*Model Name
Name of the user-defined model
Predict Output
Name of the output data
Merge Data
Predict Output will be created with predicted labels concatenated to Merge Data.
*X
User-selected columns to be X.
*Y
User-selected columns to be Y.
Inputs
*Target Data
Data Input
*Model Name
Name of the user-defined model
Predict Output
Name of the output data
Merge Data
Predict Output will be created with predicted labels concatenated to Merge Data.
*X
User-selected columns to be X
For both classifier & regressor
Inputs
*Target Data(X)
Data Input
*Model Name
Name of the user-defined model
Predict Output
Name of the output data
Merge Data
Predict Output will be created with predicted labels concatenated to Merge Data.
*X
User-selected columns to be X.
*Y
User-selected column to be Y.
In case of using Stacking & Voting models, stack the base_estimators in Arguments.
Each element of the list is defined as a tuple of string (i.e. user-defined name of the estimator) and an estimator instance.
Define the 'estimators' in Argument as below:
[('str1', estimator1),('str2', estimator2)] (ex. [('rf', RF),('gdb',GDB),('adb',ADB)] )
R-Flow Task Example Video- StackingClassifier
AdaBoostClassifier: https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html
AdaBoostRegressor: https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostRegressor.html
BaggingClassifier: https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.BaggingClassifier.html
BaggingRegressor: https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.BaggingRegressor.html
ExtraTreesClassifier: https://scikit-learn.org/0.15/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html
ExtraTreeRegressor: https://scikit-learn.org/0.15/modules/generated/sklearn.ensemble.ExtraTreesRegressor.html
GradientBoostingClassifier: https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html
GradientBoostingRegressor: https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html
IsolationForest: https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.IsolationForest.html
RandomForestClassifier: http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
RandomForestRegressor: https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html
StackingClassifier: https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.StackingClassifier.html
StackingRegressor: https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.StackingRegressor.html
VotingClassifier: https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.VotingClassifier.html
VotingRegressor: https://scikit-learn.org/0.22/modules/generated/sklearn.ensemble.VotingRegressor.html
HistGradientBoostingClassifier: https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html
HistGradientBoostingRegressor: https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html
