This workflow shows how to perform a forward feature selection on the iris data set using the preconfigured Forward Feature Selection meta node.
This workflow demonstrates the usage of the Feature Elimination Meta Node. The first input is labeled training data, the second test data which does not need to be labeled.
Inside the meta node at least a learner and a predictor node need to be inserted between the loop start and the loop end node. Alternatively a partitioning node or even a cross validation meta node can be used.
The loop iterates over all columns and iteratively removes the attribute that has the lowest influence on classification accuracy.
This workflow shows 7 methods for dimensionality reduction:
1. High ratio of missing values
2. Low variance
3. High correlation with other data columns
5. Tree ensemble based
6. Backward Elimination
7. Forward Construction