New Operator

Describe the operator

Why is this operator necessary? What does it accomplish?

This is a frequently used operator in tensorflow/keras

Can this operator be constructed using existing onnx operators?

If so, why not add it as a function?

I don't know.

Is this operator used by any model currently? Which one?

Are you willing to contribute it?

dask.data

In [12]: import dask.dataframe as dd, pandas as pd

In [13]: df = dd.from_pandas(pd.DataFrame({"A": [1, 2]}), npartitions=1)

In [14]: df.head()
/home/taugspurger/miniconda3/envs/stac-table/lib/python3.9/site-packages/dask/dataframe/core.py:6778: UserWarning: Insufficient elements for `head`. 5 elements requested, only 2 elements available. Try passing larger `npartiti

Bug/Feature Request Description

In [1]: import featuretools as ft                                                                                                                             

In [2]: es = ft.demo.load_mock_customer(return_entityset=True)                                                                                                

In [3]: import pandas as pd    

Building the doc fails for example 40_advanced/example_single_configurations on the current development branch

Logs here

...
generating gallery for examples/40_advanced... [ 50%] example_debug_logging.py

Warning, treated as error:
/home/runner/work/auto-sklearn/auto-sklearn/examples/40_advanced/example_single_configu

From the Aug 27 developer meeting.

We need to ensure that the following compositors work for multivariate forecasters, in descending order of priority

• pipeline compositors, TransformedTargetForecaster, ForecastingPipeline
• tuning compositors, ForecastingGridSearchCV, ForecastingRandomizedSearchCV
• reducers, all in the _reduce module
• multiplexing, `MultiplexFo

Hi! I was a bit surprised by the name of CVSplit. The name suggests that it is responsible for cross-validation, but the documentation reveals that it only trains and validates on one split. That doesn't match my understanding of cross-validation. It could [technically](https://en.m.wikipedia.org/wiki/Cross-va

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 RNNs. I will let this issue open for people who want to contribute to this project.
My suggestion would be just to write RNN (or any other RNN-like model) in the algorithm field in order to use an RNN model. Here is an example to illustrate what I mean:

model:
     type: classification
     algorithm: RNN
    .
    . 

**If you a

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.

<img wi