*What it does* Linear and Quadratic Discriminant Analysis are two classic classifiers with a linear and a quadratic decision surface respectively. These classifiers are fast and easy to interprete.
1 - Training input
When you choose to train a model, discriminant analysis tool expects a tabular file with numeric values, the order of the columns being as follows:
"feature_1" "feature_2" "..." "feature_n" "class_label"Example for training data The following training dataset contains 3 feature columns and a column containing class labels:
4.01163365529 -6.10797684314 8.29829894763 1 10.0788438916 1.59539821454 10.0684278289 0 -5.17607775503 -0.878286135332 6.92941850665 2 4.00975406235 -7.11847496542 9.3802423585 1 4.61204065139 -5.71217537352 9.12509610964 12 - Trainig output
Based on your choice, this tool fits a sklearn discriminant_analysis.LinearDiscriminantAnalysis or discriminant_analysis.QuadraticDiscriminantAnalysis on the traning data and outputs the trained model in the form of pickled object in a text file.
3 - Prediction input
When you choose to load a model and do prediction, the tool expects an already trained Discriminant Analysis estimator and a tabular dataset as input. The dataset is a tabular file with new samples which you want to classify. It just contains feature columns.
Example for prediction data
8.26530668997 2.96705005011 8.88881190248 2.96366327113 -3.76295851562 11.7113372463 8.13319631944 -0.223645298585 10.5820605308The number of feature columns must be the same in training and prediction datasets!
3 - Prediction output The tool predicts the class labels for new samples and adds them as the last column to the prediction dataset. The new dataset then is output as a tabular file. The prediction output format should look like the training dataset.
Discriminant Analysis is based on sklearn.discriminant_analysis library from Scikit-learn. For more information please refer to Scikit-learn site.