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| Hi Dalila, I cannot reproduce the error you see with this simple example ``` data = ResourceData["Sample Data: Fisher's Irises"]; c = Classify[data -> "Species"] FeatureImpactPlot[c] ``` Can you share a sample of the data you are using? |
| Hi Haoyu, the loss is just a function that is getting minimized during training. There is notthing special in the value 0. Typically, you can define it in a way that makes 0 mean "no error", "perfect result" or a similar concept but it's not... |
| Not perfect but you could use color based segmentation mask = Binarize[ColorDetect[img, ColorsNear[Brown]], .1] ![mask][1] Then use the centers of the morphological components HighlightImage[img, ... |
| Hi Dinesh, the benchmark is measuring the performance of raw LLMs on the task of writing WL code. The notebook assistant is using [RAG][1] based on language documentation to boost the relevance of its answers so it would be like cheating! ... |
| The issue is with the compilation step in `FunctionLayer`t not being able to read the downvalues for the `map` symbol in order to inline that in the net. This is an usupported feature at the moment. |
| Great post! It's always nice to see the technology applied to real world cases. Just one comment: the default feature extractor for `CreateSemanticSearchIndex` ("SentenceBERT") is a local model that runs on your machine. `CreateVectorDatabase`... |
| If you are interested in the information available in the panel you can get them using Information[ClassifierFunction[...]] |
| I am confused by your problem setup. A single example is `in -> out` where `in` is 192 binary digits. A single modified example is `in$mod -> out` where `in$mod` is 24 integers stored as `ByteArray` If this is correct you can use normal to... |
| The easiest way right now is by using ``` Information[p, "DecisionTree"] // Head (* Tree *) ``` However, keep in mind that this tree is not acting on the input data but on the processed data. You can force the processing pipeline to be... |
| I personally find the JAX model compelling but I never seriously worked with it. I am courious: why you would not reccomend it's inclusion? Thanks! |