Bug Report

Is the issue related to model conversion?

If the ONNX checker reports issues with this model then this is most probably related to the converter used to convert the original framework model to ONNX. Please create this bug in the appropriate converter's GitHub repo (pytorch, tensorflow-onnx, sklearn-onnx, keras-onnx, onnxmltools) to get the best help.

Describe the bug

T

Related to #8006 but the opposite side of the coin: while one can save to_zarr, it is still not possible to give a pathlib.Path object to dask.array.from_zarr, although this is possible withzarr.open.

• A better name date of birth column in our mock dataset would be birthday.

The problem I want to use auto-sklearn on is a time-series. Can we modify sklearn to include cv with time series?

This is a feature request with a good idea inside: the reduction strategies called by make_reduction should provide access to internal models via the get_fitted_params method.

Direct loopthrough does not work though, since scikit-learn does not provide a get_fitted_params method - however, the fitted estimators could be returned simply.

I want to output the regression coeffici

The current History class has some limitations: (ver 0.10.0)

1. Currently the history is saved as JSON, as a result, those recorded values are limited to simple numbers and strings. Other objects can not be saved in history files directly.
2. Saving as JSON takes lots of time and space because numbers are stored in decimal. It's getting worse when the training epoch is increasing.
3. In some

Yes

When running TabularPredictor.fit(), I encounter a BrokenPipeError for some reason.
What is causing this?
Could it be due to OOM error?

Fitting model: XGBoost ...
-34.1179 = Validation root_mean_squared_error score
10.58s = Training runtime
0.03s = Validation runtime
Fitting model: NeuralNetMXNet ...
-34.2849 = Validation root_mean_squared_error score
43.63s =

Description

We want to add support for auto ML. My suggestion is to use autokeras. I'm letting this open for newcomers who want to contribute to this project.

The parameters of the model need to be read from a yaml file (check utils.py in igel, there is a helper function to read a yaml or json file). These parameters will be used to construct and train a model. The results should be th

What's your use case?
In other words, what's your pain point?

Variable names and their icons are shown as vertical header. This

• is ugly,
• doesn't show the selection properly,
• doesn't allow sorting by variable names,
• doesn't allow selection by dragging across a range of variables (though one can drag across rows in the table itself),
• and possibly something else.

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