These WhizzML script will let the user run a fully automated
Machine Learning pipeline in BigML. It needs the AutoML Library, more info here.
From a train, holdout and test datasets, the following tasks will
be automatically done:
Unsupervised Models Generation: Creating the following
unsupervised models: Cluster, Anomaly Detector, Association
Discovery (with leverage and lift as search_strategy), PCA and
Topic Model (for datasets that contain text fields).
Feature Generation: Using the unsupervised models created
previously to append automatically generated new features to all the
user given datasets.
Feature Selection: Reducing, automatically, the number of fields
of the datasets using the Recursive Feature Eliminination
algorithm. This step is bypassed if the total number of fields is
lower than 50 (or 30 if shallow_search is True)
Model Selection Using OptiML to find the best models and using
the top 3 models to create a Fusion model to predict all the test
dataset instances. The script will return an evaluation of the final
model too.
These WhizzML script will let the user run a fully automated
Machine Learning pipeline in BigML. It needs the AutoML Library, more info here.
From a train, holdout and test datasets, the following tasks will
be automatically done:
Unsupervised Models Generation: Creating the following
unsupervised models: Cluster, Anomaly Detector, Association
Discovery (with leverage and lift as search_strategy), PCA and
Topic Model (for datasets that contain text fields).
Feature Generation: Using the unsupervised models created
previously to append automatically generated new features to all the
user given datasets.
Feature Selection: Reducing, automatically, the number of fields
of the datasets using the Recursive Feature Eliminination
algorithm. This step is bypassed if the total number of fields is
lower than 50 (or 30 if shallow_search is True)
Model Selection Using OptiML to find the best models and using
the top 3 models to create a Fusion model to predict all the test
dataset instances. The script will return an evaluation of the final
model too.
These WhizzML script will let the user run a fully automated
Machine Learning pipeline in BigML. It needs the AutoML Library, more info here.
From a train, holdout and test datasets, the following tasks will
be automatically done:
Unsupervised Models Generation: Creating the following
unsupervised models: Cluster, Anomaly Detector, Association
Discovery (with leverage and lift as search_strategy), PCA and
Topic Model (for datasets that contain text fields).
Feature Generation: Using the unsupervised models created
previously to append automatically generated new features to all the
user given datasets.
Feature Selection: Reducing, automatically, the number of fields
of the datasets using the Recursive Feature Eliminination
algorithm. This step is bypassed if the total number of fields is
lower than 50 (or 30 if shallow_search is True)
Model Selection Using OptiML to find the best models and using
the top 3 models to create a Fusion model to predict all the test
dataset instances. The script will return an evaluation of the final
model too.
