Can Ann explain results?
Artificial Neural Networks (ANN) are simplifications of the vertebrate brain, being reduced to a complex equation (the role of training is to determine the parameters of an equation that best fits the data). Can a mathematical function explain its output? The answer is no.
Can random numbers predict?
Yes, it is possible to predict what number a random number generator will produce next. I’ve seen this called cracking, breaking, or attacking the RNG. Searching for any of those terms along with “random number generator” should turn up a lot of results.
What is the output of Softmax?
The softmax function will output a probability of class membership for each class label and attempt to best approximate the expected target for a given input. For example, if the integer encoded class 1 was expected for one example, the target vector would be: [0, 1, 0]
Can a neural network prediction score be a probabilities?
For example, outputs with a score of .75 should be correct 75 % of the time, no more, no less. This isn’t usually the case with neural networks and it’s easy to show. Just adding a softmax activation does not magically turn outputs into probabilities.
Why do I care about the accuracy of neural networks?
The below image shows the proportion of correct predictions vs the output scores for a very simple neural network. As models and training algorithms get more complex, the outputs typically diverge further from ideal probability estimates. Why do I care? Improving the accuracy of Machine Learning model predictions is the subject of much study.
How to convert the output of an artificial neural network into?
This would tend to be the case if the transfer function (or output function) in both the preceding stage and providing the final output is in the 0 to 1 range too (typically the sigmoidal logistic function). However, there is no guarantee that it will but repairs are possible.
What is the activation value of a neuron?
The activation value of a single output neuron is a linearly weighted sum, and may be directly interpreted as an approximate probability if the network is trained to give outputs a range from 0 to 1.