Scikit Models

Scikit-learn provides a range of supervised and unsupervised learning algorithms via a consistent interface in Python.

The Algorithm types supported by Scikit are:

  • Regression

  • Classification

  • Clustering

  • Pipeline

Score the Model

To use a Scikit model for scoring, drag Scikit processor from analytics section onto the pipeline canvas and right-click on it for further configuration.

Scikit Model Configuration:

FieldDescription
AlgorithmAll predefined algorithms will be listed here.Select the algorithm on the basis of which prediction has to be done.
Model NameAll the registered models of selected Algorithm will be listed here. Select the model which is to be used for prediction.

Score the Model Using H2O

To use a H2O model for scoring, drag H2O processor from analytics section onto the pipeline canvas and right-click on it for further configurations.

H2O Model Configuration:

FieldDescription
AlgorithmAll predefined algorithms will be listed here.Select the algorithm on the basis of which prediction has to be done.
Model NameAll the registered models of selected Algorithm will be listed here. Select the model which is to be used for prediction.
Output FieldVariable which holds the predicted output of model.

If you have any feedback on Gathr documentation, please email us!

← H2O Models
Models Listing Page →