Compute a Neural Network Model or a Multi Nomial Logistic Model.
Neural Networks are popular in datamining and analytics, mainly thanks to some of its promoters (i.e. Google) and their increased popularity in image pattern recognition. There now exists a new type of Neural Network algorithms (that is named “deep neural network”) that seems to perform reasonably well on image classification and segmentation tasks.
The action described in this section is not a “deep” neural network: it’s an “old-school” Neural Network algorithm and it’s included in Anatella mainly because of completeness (and for explanatory/teaching purposes). More precisely, “old-school” Neural Network algorithms are usually not very useful because they are notoriously difficult to adjust properly to get a correct classification accuracy (although it’s sometime possible to get good results, it’s quite difficult).
List of Predictors: Select independent variables
Target: Select the variable you want to predict
Model Output: Set the file name for the model results
Export to PMML: explort to a PMML file to include in other tools.
Select Classification Model: either Multinomial Logit or Neural Network
Base: set the base category
Number of perceptrons for Neural Networks: Manually set the perceptrons. This implementation uses only one layer
Maximum Iterations: how long are you willing to wait for results. 200 is a good number.
Linear Model: Specify if the perceptrons should use linear models instead of logistics. This works only for NNET and allows estimating continuous targets, often with higher precision than simple linear models.
Show Plots: choose to show or not the visuals of the nnet or MNL model
Include Prediction in first Output: fit the model so you can better assess its quality
Normalize Predictors: make sure all variables are normalized. This usually yields better results.