Hello,
Considering your amazing efficiency on pandas, numpy, and more, it would seem to make sense for your module to work with even bigger data, such as Audio (for example .mp3 and .wav). This is something that would help a lot considering the nature audio (ie. where one of the lowest and most common sampling rates is still 44,100 samples/sec). For a use case, I would consider vaex.open('Hu

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 =

Feature request

As requested by some, and as @ekamioka started on this PR #244. It might be interesting to get some helper functions to use embeddings as it's not the simplest concept in deep learning.

What is the expected behavior?
Calling a few helper function to get all the correct parameters before using TabNet

Example:
In the image below the word starships should begin on a new line to avoid being split.

Terminal width is provided to determine how many columns to print. The terminal width or the total width of the column headers may be used to wrap the text in the footer.

🚀 Feature

To do classification you might sometimes only have numerical fields. Today the closest you can do is with tabular classification. However it does expect to have categorical fields.

~/conda/lib/python3.8/site-packages/numpy/core/shape_base.py in stack(arrays, axis, out)
    421     arrays = [asanyarray(arr) for arr in arrays]
    422     if not arrays:
--> 423         raise

tf.keras.Sequential is an instance of tf.keras.Model so could be left out across types in alibi-detect.

In sklearn cross validation function, we can pass group parameter. Looking for this option here,

It would be helpful if the progress bar for model fitting could be disabled. This is particularly relevant when trying to optimize model hyperparameters, when the following occurs:

Passing a disable_pbar or similar flag to `f

🚀 Feature request

The original PyTorch implementation of TabularDropout transformation is available at transformers4rec/torch/tabular/transformations.py