This workflow is an example of how to build a basic prediction / classification model using a decision tree. Dataset describes wine chemical features. Output class is wine color: red/white.

A simple example using a Naive Bayes learner and predictor to classify some shuttle data. For more background information see" http://archive.ics.uci.edu/ml/datasets/Statlog+(Shuttle)

The workflow learns a decision tree on a data set and applies the model on a new data set, whereby the distribution is shown in small histogram depiction.

This workflow shows how to learn a Gradient Boosted Trees model on the adult data set.

After the data is normalized and partitioned, Multi-Layer-Perzeptron (MLP) is trained and applied.

This workflow is an example of how to build a basic prediction / classification model using logistic regression.

After the data is partitioned into train and test set, a decision tree model is trained and applied.