..  datamining-chapter:

Data Mining Methods
===================
We define the  step to go from the features to a radiomics model, whether it is
classification, regression, or survival, as data mining. Within this step,
combined model selection and hyperparameter optimization is performed to find
the best performing workflow, i.e. combination of models and hyperparameters.

In this part of the documentation, all methods available in the data mining
step are described in the order they occur. The actual single workflow
fitting and scoring is done in :py:mod:`WORC.WORC.classification.fitandscore.fit_and_score`,
in which all of these methods are embedded.

Here, we provide a rationale for the methods. For a comprehensive overview
of all functions and parameters, please look at
:ref:`the config chapter <config-chapter>`.

OneHotEncoding
---------------
Documentation WIP.

Imputation
---------------
Documentation WIP.

Feature Selection: Groupwise
----------------------------
Documentation WIP.

Feature Scaling
---------------
Documentation WIP.

Feature Selection: Variance
----------------------------
Documentation WIP.

Feature Selection: Univariate Statistical Test
-----------------------------------------------
Documentation WIP.

Feature Selection: Relief
-----------------------------------------------
Documentation WIP.

Feature Selection: Select from model
-----------------------------------------------
Documentation WIP.

Dimensionality Reduction: principal component analysis (PCA)
-------------------------------------------------------------
Documentation WIP.

Resampling
-----------
Documentation WIP.

Machine Learning
-----------------
Documentation WIP.

Classification
^^^^^^^^^^^^^^^
Documentation WIP.

Regression
^^^^^^^^^^^^
Documentation WIP.

Survival
^^^^^^^^^^
Documentation WIP.
