# 1.11. Feature selection¶

The classes in the `sklearn.feature_selection` module can be used
for feature selection/dimensionality reduction on sample sets, either to
improve estimators’ accuracy scores or to boost their performance on very
high-dimensional datasets.

## 1.11.1. Removing features with low variance¶

`VarianceThreshold` is a simple baseline approach to feature selection.
It removes all features whose variance doesn’t meet some threshold.
By default, it removes all zero-variance features,
i.e. features that have the same value in all samples.

As an example, suppose that we have a dataset with boolean features, and we want to remove all features that are either one or zero (on or off) in more than 80% of the samples. Boolean features are Bernoulli random variables, and the variance of such variables is given by

so we can select using the threshold `.8 * (1 - .8)`:

```
>>> from sklearn.feature_selection import VarianceThreshold
>>> X = [[0, 0, 1], [0, 1, 0], [1, 0, 0], [0, 1, 1], [0, 1, 0], [0, 1, 1]]
>>> sel = VarianceThreshold(threshold=(.8 * (1 - .8)))
>>> sel.fit_transform(X)
array([[0, 1],
[1, 0],
[0, 0],
[1, 1],
[1, 0],
[1, 1]])
```

As expected, `VarianceThreshold` has removed the first column,
which has a probability of containing a one.

## 1.11.2. Univariate feature selection¶

Univariate feature selection works by selecting the best features based on
univariate statistical tests. It can be seen as a preprocessing step
to an estimator. Scikit-learn exposes feature selection routines
as objects that implement the `transform` method:

SelectKBestremoves all but the highest scoring features

SelectPercentileremoves all but a user-specified highest scoring percentage of featuresusing common univariate statistical tests for each feature: false positive rate

SelectFpr, false discovery rateSelectFdr, or family wise errorSelectFwe.

GenericUnivariateSelectallows to perform univariate featureselection with a configurable strategy. This allows to select the best univariate selection strategy with hyper-parameter search estimator.

For instance, we can perform a test to the samples to retrieve only the two best features as follows:

```
>>> from sklearn.datasets import load_iris
>>> from sklearn.feature_selection import SelectKBest
>>> from sklearn.feature_selection import chi2
>>> iris = load_iris()
>>> X, y = iris.data, iris.target
>>> X.shape
(150, 4)
>>> X_new = SelectKBest(chi2, k=2).fit_transform(X, y)
>>> X_new.shape
(150, 2)
```

These objects take as input a scoring function that returns univariate p-values:

- For regression:
f_regression- For classification:
chi2orf_classif

Feature selection with sparse data

If you use sparse data (i.e. data represented as sparse matrices),
only `chi2` will deal with the data without making it dense.

Warning

Beware not to use a regression scoring function with a classification problem, you will get useless results.

Examples:

## 1.11.3. Recursive feature elimination¶

Given an external estimator that assigns weights to features (e.g., the
coefficients of a linear model), recursive feature elimination (`RFE`)
is to select features by recursively considering smaller and smaller sets of
features. First, the estimator is trained on the initial set of features and
weights are assigned to each one of them. Then, features whose absolute weights
are the smallest are pruned from the current set features. That procedure is
recursively repeated on the pruned set until the desired number of features to
select is eventually reached.

`RFECV` performs RFE in a cross-validation loop to find the optimal
number of features.

Examples:

*Recursive feature elimination*: A recursive feature elimination example showing the relevance of pixels in a digit classification task.*Recursive feature elimination with cross-validation*: A recursive feature elimination example with automatic tuning of the number of features selected with cross-validation.

## 1.11.4. L1-based feature selection¶

### 1.11.4.1. Selecting non-zero coefficients¶

*Linear models* penalized with the L1 norm have
sparse solutions: many of their estimated coefficients are zero. When the goal
is to reduce the dimensionality of the data to use with another classifier,
they expose a `transform` method to select the non-zero coefficient. In
particular, sparse estimators useful for this purpose are the
`linear_model.Lasso` for regression, and
of `linear_model.LogisticRegression` and `svm.LinearSVC`
for classification:

```
>>> from sklearn.svm import LinearSVC
>>> from sklearn.datasets import load_iris
>>> iris = load_iris()
>>> X, y = iris.data, iris.target
>>> X.shape
(150, 4)
>>> X_new = LinearSVC(C=0.01, penalty="l1", dual=False).fit_transform(X, y)
>>> X_new.shape
(150, 3)
```

With SVMs and logistic-regression, the parameter C controls the sparsity: the smaller C the fewer features selected. With Lasso, the higher the alpha parameter, the fewer features selected.

Examples:

*Classification of text documents using sparse features*: Comparison of different algorithms for document classification including L1-based feature selection.

**L1-recovery and compressive sensing**

For a good choice of alpha, the *Lasso* can fully recover the
exact set of non-zero variables using only few observations, provided
certain specific conditions are met. In particular, the number of
samples should be “sufficiently large”, or L1 models will perform at
random, where “sufficiently large” depends on the number of non-zero
coefficients, the logarithm of the number of features, the amount of
noise, the smallest absolute value of non-zero coefficients, and the
structure of the design matrix X. In addition, the design matrix must
display certain specific properties, such as not being too correlated.

There is no general rule to select an alpha parameter for recovery of
non-zero coefficients. It can by set by cross-validation
(`LassoCV` or `LassoLarsCV`), though this may lead to
under-penalized models: including a small number of non-relevant
variables is not detrimental to prediction score. BIC
(`LassoLarsIC`) tends, on the opposite, to set high values of
alpha.

**Reference** Richard G. Baraniuk “Compressive Sensing”, IEEE Signal
Processing Magazine [120] July 2007
http://dsp.rice.edu/files/cs/baraniukCSlecture07.pdf

### 1.11.4.2. Randomized sparse models¶

The limitation of L1-based sparse models is that faced with a group of very correlated features, they will select only one. To mitigate this problem, it is possible to use randomization techniques, reestimating the sparse model many times perturbing the design matrix or sub-sampling data and counting how many times a given regressor is selected.

`RandomizedLasso` implements this strategy for regression
settings, using the Lasso, while `RandomizedLogisticRegression` uses the
logistic regression and is suitable for classification tasks. To get a full
path of stability scores you can use `lasso_stability_path`.

Note that for randomized sparse models to be more powerful than standard F statistics at detecting non-zero features, the ground truth model should be sparse, in other words, there should be only a small fraction of features non zero.

Examples:

*Sparse recovery: feature selection for sparse linear models*: An example comparing different feature selection approaches and discussing in which situation each approach is to be favored.

References:

- N. Meinshausen, P. Buhlmann, “Stability selection”, Journal of the Royal Statistical Society, 72 (2010) http://arxiv.org/pdf/0809.2932
- F. Bach, “Model-Consistent Sparse Estimation through the Bootstrap” http://hal.inria.fr/hal-00354771/

## 1.11.5. Tree-based feature selection¶

Tree-based estimators (see the `sklearn.tree` module and forest
of trees in the `sklearn.ensemble` module) can be used to compute
feature importances, which in turn can be used to discard irrelevant
features:

```
>>> from sklearn.ensemble import ExtraTreesClassifier
>>> from sklearn.datasets import load_iris
>>> iris = load_iris()
>>> X, y = iris.data, iris.target
>>> X.shape
(150, 4)
>>> clf = ExtraTreesClassifier()
>>> X_new = clf.fit(X, y).transform(X)
>>> clf.feature_importances_
array([ 0.04..., 0.05..., 0.4..., 0.4...])
>>> X_new.shape
(150, 2)
```

Examples:

*Feature importances with forests of trees*: example on synthetic data showing the recovery of the actually meaningful features.*Pixel importances with a parallel forest of trees*: example on face recognition data.

## 1.11.6. Feature selection as part of a pipeline¶

Feature selection is usually used as a pre-processing step before doing
the actual learning. The recommended way to do this in scikit-learn is
to use a `sklearn.pipeline.Pipeline`:

```
clf = Pipeline([
('feature_selection', LinearSVC(penalty="l1")),
('classification', RandomForestClassifier())
])
clf.fit(X, y)
```

In this snippet we make use of a `sklearn.svm.LinearSVC`
to evaluate feature importances and select the most relevant features.
Then, a `sklearn.ensemble.RandomForestClassifier` is trained on the
transformed output, i.e. using only relevant features. You can perform
similar operations with the other feature selection methods and also
classifiers that provide a way to evaluate feature importances of course.
See the `sklearn.pipeline.Pipeline` examples for more details.