Discussing the article: "MQL5 Wizard Techniques you should know (Part 31): Selecting the Loss Function"
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Check out the new article: MQL5 Wizard Techniques you should know (Part 31): Selecting the Loss Function.
Loss Function is the key metric of machine learning algorithms that provides feedback to the training process by quantifying how well a given set of parameters are performing when compared to their intended target. We explore the various formats of this function in an MQL5 custom wizard class.
MQL5 wizard can be a test bed for a wide variety of ideas, as we have covered so far in these series. And every once in a while, one is presented with a custom signal that has more than one way of being implemented. We looked at this scenario in the 2 articles about learning rates, as well as the last article on batch normalization. Each of those aspects to machine learning presented more than one potential custom signal, as was discussed. The loss , also by virtue of having multiple formats, is in a similar situation.
The way in which a test run result is compared to its target does not have a single method. If we consider the enumerations available in
ENUM_LOSS_FUNCTION enumeration in MQL5, they are 14, and this list is not even exhaustive. Does this mean that every one of them offers a distinct way at training in machine learning? Probably not, but the point is there are differences, some nuanced, and these differences can often imply you need to carefully select your loss function depending on the nature of the network or algorithm you are training.
Author: Stephen Njuki