Mads Dabros

Hi @MichaelBenAssor, Originally I decided to leave this feature out of the RandomForest implementation to keep memory consumption low, and leave the option of getting an unbiased estimate of the...

Hi @ZenBoer , I have considered to expand the learner interface to something like `Learn(Tensor observations, Tensor targets)`, to support a wider range of problems. This would be most relevant...

Hi @gaop123 , It could sound like a GPU memory issue. There are no special compilation needed once you have installed Cuda like descriped in the guide: [Cuda installation](https://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/index.html). Could...

Hi @gaop123 , If you are creating a classification model, you can get the confidence of the GBM by using the `PredictProbability` method on the model: ```csharp /// Single new...

@gaop123 Ahh yes, that is something different indeed. Currently, there are no build in methods for dealing with classes unknown at training time. There are a few ways you can...

Hi @Genbox, I'm not sure how much time the learners spent in the random generator during training. But if it is a significant part of the training time, it could...

@AndreiDegtiarev This is not an issue I have encountered before. I can't see why SharpLearning should depend on `Microsoft.IdentityModel.Clients.ActiveDirectory`, so it must be something implicit via another dependency. Is there...

Hi @david-clinch, I am glad you find SharpLearning useful - thanks! You are welcome to create a pull request with a fix, and a more explanatory error message, that would...

Hi @gilnahmias , Thanks for using SharpLearning, I am glad you like the library. There are some limited methods for handling categorical data in the `SharpLearning.FeatureTransformations` project. You can see...

Hi @festith, Currently PicNet.XGBoost is the handling the interop with XGBoost, so the quickest route to having this fixed is probably to open an issue in [XGBoost.Net](https://github.com/PicNet/XGBoost.Net) and have them...