mlr3pipelines
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mlr-org
Dataflow Programming for Machine Learning in R
This adds an informative error message to `PipeOpSmote` for the case in which a task with columns is passed to the pipeop that SMOTE does not know how to handle,...
https://github.com/yandex-research/rtdl-num-embeddings/blob/abf8a8b35854e4b06476bb48902096b0b58ffce2/package/rtdl_num_embeddings.py#L300 https://arxiv.org/abs/2203.05556# Could either be its own pipeop or an extension of an existing binning pipeop
This implements Uniform Manifold Approximation and Projection (UMAP) from the [uwot](https://cran.r-project.org/web/packages/uwot/index.html) package. Training works via `uwot::umap2()` and prediction through `uwot::umap_transform()`. closes https://github.com/mlr-org/mlr3pipelines/issues/755
See https://stackoverflow.com/questions/78888318/how-can-i-use-smote-upsampling-with-a-categorical-variable-that-is-used-only-for
what other methods exist in the themis package that we could use?
GraphLearner$pipeops_param_set (named according to pipeops, forwards to pipeop's param_set) GraphLearner$pipeops_param_set_values (named according to pipeops, forwards to pipeop's param_set$values)
The documentation says ``` classweights adds a class weight column. Sample weights are added to each sample according to the target class. Only binary classification tasks are supported. ``` Then,...
