Techniques for Dimensionality Reduction

This workflow performs classification on data sets that were reduced using the following dimensionality reduction techniques: - Linear Discriminant Analysis (LDA) - Auto-encoder - t-SNE - Missing values ratio - Low variance filter - High correlation filter - Ensemble tree - PCA - Backward feature elimination - Forward feature selection --- The performances of the classification models are compared to the performance that is achieved when all columns are retained in terms of overall accuracy and AuC statistics. These evaluation metrics are produced by the best performing classification model out of this bag of models: - Multilayer Feedforward Neural Networks - Naive Bayes - Decision Tree

This is a companion discussion topic for the original entry at

i used LDA nodes in my dataset, but i’ve got error with report “Execute failed: The size of the smallest group must be larger than the number of predictor variables (9). Class “Benin” has only 1 non-missing instance. Please reduce the number of selected predictor variables”

could you please explain about that errors ? and what should i do ?