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mfeurer merged 1 commit into from
May 12, 2022
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Re-organize docs to advertise new text features #1468

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24 changes: 8 additions & 16 deletions doc/index.rst
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Expand Up @@ -25,22 +25,10 @@ the technology behind *auto-sklearn* by reading our paper published at
`NeurIPS 2015 <https://papers.neurips.cc/paper/5872-efficient-and-robust-automated-machine-learning.pdf>`_
.

.. topic:: NEW: Auto-sklearn 2.0
.. topic:: NEW: Text feature support

Auto-sklearn 2.0 includes latest research on automatically configuring the AutoML system itself
and contains a multitude of improvements which speed up the fitting the AutoML system.

*auto-sklearn 2.0* works the same way as regular *auto-sklearn* and you can use it via

.. code:: python

from autosklearn.experimental.askl2 import AutoSklearn2Classifier

A paper describing our advances is available on `arXiv <https://arxiv.org/abs/2007.04074>`_.

.. topic:: NEW: Material from tutorials and presentations

We provide slides and notebooks from talks and tutorials here: `auto-sklearn-talks <https://github.com/automl/auto-sklearn-talks>`_
Auto-sklearn now supports text features, check our new example:
:ref:`sphx_glr_examples_40_advanced_example_text_preprocessing.py`


Example
Expand All @@ -64,7 +52,6 @@ Example

This will run for one hour and should result in an accuracy above 0.98.


Manual
******

Expand All @@ -74,6 +61,11 @@ Manual
* :ref:`extending`
* :ref:`faq`

Additional Material
*******************

We provide slides and notebooks from talks and tutorials here:
`auto-sklearn-talks <https://github.com/automl/auto-sklearn-talks>`_

License
*******
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19 changes: 19 additions & 0 deletions doc/manual.rst
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Expand Up @@ -19,6 +19,25 @@ give short explanations (click the title to expand text), e.g.

We provide resources for talks, tutorials and presentations on *auto-sklearn* under `auto-sklearn-talks <https://github.com/automl/auto-sklearn-talks>`_

.. _askl2:

Auto-sklearn 2.0
================

Auto-sklearn 2.0 includes latest research on automatically configuring the AutoML system itself
and contains a multitude of improvements which speed up the fitting the AutoML system.
Concretely, Auto-sklearn 2.0 automatically sets the :ref:`bestmodel`, decides whether it can use
the efficient bandit strategy *Successive Halving* and uses meta-feature free *Portfolios* for
efficient meta-learning.

*auto-sklearn 2.0* has the same interface as regular *auto-sklearn* and you can use it via

.. code:: python

from autosklearn.experimental.askl2 import AutoSklearn2Classifier

A paper describing our advances is available on `arXiv <https://arxiv.org/abs/2007.04074>`_.

.. _limits:

Resource limits
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