softmax

#Softmax

##Description The Softmax Classifier is a simple but effective classifier that works well on problems that are linearly separable.

The Softmax algorithm is part of the GRT classification modules.

##Advantages The Softmax algorithm is a good algorithm to use for the classification of basic problems that are linearly separable. The main advantage of the Softmax Classifier is that it consists of a very simple model and is therefore very fast to train and predict.

##Disadvantages The main limitation of the Softmax algorithm is that it will not work if your data is not linearly separable.

Another limitation of the Softmax algorithm is that it does not support null rejection, so you need to train the algorithm with a specific null class if you need one. If you want to use a classifier that supports automatic null rejection, then you might want to try either the Adaptive Naive Bayes Classifier, [[MinDist|mindist][, or [[KNN|knn]] algorithms.

##Training Data Format You should use the ClassificationData data structure to train the Softmax classifier.

##Example Code Softmax Example