Added AdaBoost Classification with FlAML framework.
Testing
Choosing dataset and Classification ML workflow
Select data:
Here we select column 2 "Label" for Y set, column [3,12] as X set.
Then choose mode2: Classification. Here we can see "AdaBoost" in label 11:
In feature engineering part we get the data.
AdaBoost in non-autoML workflow
Select parameters for AdaBoost. Here we expose 3 hyperparameter in the model: N estimator for how many decision trees we are going to use, Learning rate for the model, max_depth for the depth of the decision trees used in AdaBoost.
Added AdaBoost Classification with FlAML framework.
Testing
Choosing dataset and Classification ML workflow
Select data:
Here we select column 2 "Label" for Y set, column [3,12] as X set.
Then choose mode2: Classification. Here we can see "AdaBoost" in label 11:
In feature engineering part we get the data.
AdaBoost in non-autoML workflow
Select parameters for AdaBoost. Here we expose 3 hyperparameter in the model: N estimator for how many decision trees we are going to use, Learning rate for the model, max_depth for the depth of the decision trees used in AdaBoost.
Training and collecting result:
AutoML Workflow
Select AutoML here:
Auto tuning:
Final Result: