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Introduction

  • You can run Python based ML models and the B2Metric AutoML engine.
  • You can run Supervised based classification and regression models and unsupervised based clustering and anomaly detection models.
  • You can view the models and the outputs of the models.

To create a new experiment model, the steps you need to follow are:

  • Choosing the data,
  • Model type selection (supervised or unsupervised)
  • Choosing the target variable that you want to predict (for supervised based modeling),
  • Choosing the algorithm that you want to use.

Then you can start a new experiment.

In the B2Metric ML Studio modeling module, you can see some extra steps before starting a supervised based experiment. Like input selection, hyperparameter optimization, and feature extraction. These steps are not required for modeling but they are optional AutoML modeling phases.