Iris flower classification with MLP using MATLAB.

1. sepal length in cm
2. sepal width in cm
3. petal length in cm
4. petal width in cm
5. class: Iris Setosa, Iris Versicolour and Iris Virginica.

• Setosa = [1 0 0 ]
• Versicolor = [0 1 0]
• Virginica = [0 0 1]

Train = 70%, Validation = 15% and Testing = 15%
Number hidden of nodes = 4 
Epochs = 1000
Trainng Function = trainlm
Transfer Function (layer 1) = tansig
Trasnfer Function (layer 2) = purelin

Accuracy = 99.3%

In fisherIris_mpl_kfold.m the dataset was divided into 10 folds. Each k-folds has size 15x5.

The best configuration obtained from the network with the cross validation technique was:

Number hidden of nodes = 4 
Epochs = 1000
Trainng Function = trainlm
Transfer Function (layer 1) = tansig
Trasnfer Function (layer 2) = purelin

-- Average accuracy = 94.667%

https://la.mathworks.com/help/deeplearning/gs/classify-patterns-with-a-neural-network.html https://la.mathworks.com/help/deeplearning/ref/plotconfusion.html https://la.mathworks.com/help/deeplearning/ref/patternnet.html