Getting Started
===============

The purpose of this guide is to illustrate some of the main features that
``scikit-learn`` provides. It assumes a very basic working knowledge of
machine learning practices (model fitting, predicting, cross-validation,
etc.). Please refer to our :ref:`installation instructions
<installation-instructions>` for installing ``scikit-learn``.

``Scikit-learn`` is an open source machine learning library that supports
supervised and unsupervised learning. It also provides various tools for
model fitting, data preprocessing, model selection, model evaluation,
and many other utilities.

Fitting and predicting: estimator basics
----------------------------------------

``Scikit-learn`` provides dozens of built-in machine learning algorithms and
models, called :term:`estimators`. Each estimator can be fitted to some data
using its :term:`fit` method.

Here is a simple example where we fit a
:class:`~sklearn.ensemble.RandomForestClassifier` to some very basic data::

  >>> from sklearn.ensemble import RandomForestClassifier
  >>> clf = RandomForestClassifier(random_state=0)
  >>> X = [[ 1,  2,  3],  # 2 samples, 3 features
  ...      [11, 12, 13]]
  >>> y = [0, 1]  # classes of each sample
  >>> clf.fit(X, y)
  RandomForestClassifier(random_state=0)

The :term:`fit` method generally accepts 2 inputs:

- The samples matrix (or design matrix) :term:`X`. The size of ``X``
  is typically ``(n_samples, n_features)``, which means that samples are
  represented as rows and features are represented as columns.
- The target values :term:`y` which are real numbers for regression tasks, or
  integers for classification (or any other discrete set of values). For
  unsupervised learning tasks, ``y`` does not need to be specified. ``y`` is
  usually a 1d array where the ``i`` th entry corresponds to the target of the
  ``i`` th sample (row) of ``X``.

Both ``X`` and ``y`` are usually expected to be numpy arrays or equivalent
:term:`array-like` data types, though some estimators work with other
formats such as sparse matrices.

Once the estimator is fitted, it can be used for predicting target values of
new data. You don't need to re-train the estimator::

  >>> clf.predict(X)  # predict classes of the training data
  array([0, 1])
  >>> clf.predict([[4, 5, 6], [14, 15, 16]])  # predict classes of new data
  array([0, 1])

Transformers and pre-processors
-------------------------------

Machine learning workflows are often composed of different parts. A typical
pipeline consists of a pre-processing step that transforms or imputes the
data, and a final predictor that predicts target values.

In ``scikit-learn``, pre-processors and transformers follow the same API as
the estimator objects (they actually all inherit from the same
``BaseEstimator`` class). The transformer objects don't have a
:term:`predict` method but rather a :term:`transform` method that outputs a
newly transformed sample matrix ``X``::

  >>> from sklearn.preprocessing import StandardScaler
  >>> X = [[0, 15],
  ...      [1, -10]]
  >>> # scale data according to computed scaling values
  >>> StandardScaler().fit(X).transform(X)
  array([[-1.,  1.],
         [ 1., -1.]])

Sometimes, you want to apply different transformations to different features:
the :ref:`ColumnTransformer<column_transformer>` is designed for these
use-cases.

Pipelines: chaining pre-processors and estimators
--------------------------------------------------

Transformers and estimators (predictors) can be combined together into a
single unifying object: a :class:`~sklearn.pipeline.Pipeline`. The pipeline
offers the same API as a regular estimator: it can be fitted and used for
prediction with ``fit`` and ``predict``. As we will see later, using a
pipeline will also prevent you from data leakage, i.e. disclosing some
testing data in your training data.

In the following example, we :ref:`load the Iris dataset <datasets>`, split it
into train and test sets, and compute the accuracy score of a pipeline on
the test data::

  >>> from sklearn.preprocessing import StandardScaler
  >>> from sklearn.linear_model import LogisticRegression
  >>> from sklearn.pipeline import make_pipeline
  >>> from sklearn.datasets import load_iris
  >>> from sklearn.model_selection import train_test_split
  >>> from sklearn.metrics import accuracy_score
  ...
  >>> # create a pipeline object
  >>> pipe = make_pipeline(
  ...     StandardScaler(),
  ...     LogisticRegression()
  ... )
  ...
  >>> # load the iris dataset and split it into train and test sets
  >>> X, y = load_iris(return_X_y=True)
  >>> X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
  ...
  >>> # fit the whole pipeline
  >>> pipe.fit(X_train, y_train)
  Pipeline(steps=[('standardscaler', StandardScaler()),
                  ('logisticregression', LogisticRegression())])
  >>> # we can now use it like any other estimator
  >>> accuracy_score(pipe.predict(X_test), y_test)
  0.97...

Model evaluation
----------------

Fitting a model to some data does not entail that it will predict well on
unseen data. This needs to be directly evaluated. We have just seen the
:func:`~sklearn.model_selection.train_test_split` helper that splits a
dataset into train and test sets, but ``scikit-learn`` provides many other
tools for model evaluation, in particular for :ref:`cross-validation
<cross_validation>`.

We here briefly show how to perform a 5-fold cross-validation procedure,
using the :func:`~sklearn.model_selection.cross_validate` helper. Note that
it is also possible to manually iterate over the folds, use different
data splitting strategies, and use custom scoring functions. Please refer to
our :ref:`User Guide <cross_validation>` for more details::

  >>> from sklearn.datasets import make_regression
  >>> from sklearn.linear_model import LinearRegression
  >>> from sklearn.model_selection import cross_validate
  ...
  >>> X, y = make_regression(n_samples=1000, random_state=0)
  >>> lr = LinearRegression()
  ...
  >>> result = cross_validate(lr, X, y)  # defaults to 5-fold CV
  >>> result['test_score']  # r_squared score is high because dataset is easy
  array([1., 1., 1., 1., 1.])

Automatic parameter searches
----------------------------

All estimators have parameters (often called hyper-parameters in the
literature) that can be tuned. The generalization power of an estimator
often critically depends on a few parameters. For example a
:class:`~sklearn.ensemble.RandomForestRegressor` has a ``n_estimators``
parameter that determines the number of trees in the forest, and a
``max_depth`` parameter that determines the maximum depth of each tree.
Quite often, it is not clear what the exact values of these parameters
should be since they depend on the data at hand.

``Scikit-learn`` provides tools to automatically find the best parameter
combinations (via cross-validation). In the following example, we randomly
search over the parameter space of a random forest with a
:class:`~sklearn.model_selection.RandomizedSearchCV` object. When the search
is over, the :class:`~sklearn.model_selection.RandomizedSearchCV` behaves as
a :class:`~sklearn.ensemble.RandomForestRegressor` that has been fitted with
the best set of parameters. Read more in the :ref:`User Guide
<grid_search>`::

  >>> from sklearn.datasets import fetch_california_housing
  >>> from sklearn.ensemble import RandomForestRegressor
  >>> from sklearn.model_selection import RandomizedSearchCV
  >>> from sklearn.model_selection import train_test_split
  >>> from scipy.stats import randint
  ...
  >>> X, y = fetch_california_housing(return_X_y=True)
  >>> X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
  ...
  >>> # define the parameter space that will be searched over
  >>> param_distributions = {'n_estimators': randint(1, 5),
  ...                        'max_depth': randint(5, 10)}
  ...
  >>> # now create a searchCV object and fit it to the data
  >>> search = RandomizedSearchCV(estimator=RandomForestRegressor(random_state=0),
  ...                             n_iter=5,
  ...                             param_distributions=param_distributions,
  ...                             random_state=0)
  >>> search.fit(X_train, y_train)
  RandomizedSearchCV(estimator=RandomForestRegressor(random_state=0), n_iter=5,
                     param_distributions={'max_depth': ...,
                                          'n_estimators': ...},
                     random_state=0)
  >>> search.best_params_
  {'max_depth': 9, 'n_estimators': 4}

  >>> # the search object now acts like a normal random forest estimator
  >>> # with max_depth=9 and n_estimators=4
  >>> search.score(X_test, y_test)
  0.73...

.. note::

    In practice, you almost always want to :ref:`search over a pipeline
    <composite_grid_search>`, instead of a single estimator. One of the main
    reasons is that if you apply a pre-processing step to the whole dataset
    without using a pipeline, and then perform any kind of cross-validation,
    you would be breaking the fundamental assumption of independence between
    training and testing data. Indeed, since you pre-processed the data
    using the whole dataset, some information about the test sets are
    available to the train sets. This will lead to over-estimating the
    generalization power of the estimator (you can read more in this `Kaggle
    post <https://www.kaggle.com/alexisbcook/data-leakage>`_).

    Using a pipeline for cross-validation and searching will largely keep
    you from this common pitfall.


Next steps
----------

We have briefly covered estimator fitting and predicting, pre-processing
steps, pipelines, cross-validation tools and automatic hyper-parameter
searches. This guide should give you an overview of some of the main
features of the library, but there is much more to ``scikit-learn``!

Please refer to our :ref:`user_guide` for details on all the tools that we
provide. You can also find an exhaustive list of the public API in the
:ref:`api_ref`.

You can also look at our numerous :ref:`examples <general_examples>` that
illustrate the use of ``scikit-learn`` in many different contexts.

The :ref:`tutorials <tutorial_menu>` also contain additional learning
resources.
