Excuse me for my ignorance, as I am learning the theory behind models and in this case neural nets. I trying to train a model using the library(nnet) library(NeuralNetTools) packages.
For example, if my code was:
#training model on training dataset
nnet_model <- nnet(Morphology~. ,size=10,data=morph_scaled_train, maxit=1500)
#generating predictions
nnet_prediction_prob <- predict(nnet_model,morph_test)
This gives the prediction output as numbers. I.e.
Blue Red Green Yellow
1 1.020685e-180 1.000000e+00 4.496185e-255 4.079526e-254
2 1.020685e-180 1.000000e+00 4.496185e-255 4.079526e-254
3 1.020685e-180 1.000000e+00 4.496185e-255 4.079526e-254
How could I convert this to a factor i.e. Row 1 is Red, row 2 is blue for example ... Giving the overall prediction. When I use predict function for other models e.g. random forest, logistic regression, it gives it as the overall prediction and not numbers, and this is what I wish for nnets. Is this possible? Or just the way the model works this cannot be interpreted. Thanks!
Use max.col:
colnames(nnet_prediction_prob)[max.col(nnet_prediction_prob)]
[1] "Red" "Red" "Red"