# Model selection: choosing estimators and their parameters¶

## Score, and cross-validated scores¶

As we have seen, every estimator exposes a `score`

method that can judge
the quality of the fit (or the prediction) on new data. **Bigger is
better**.

```
>>> from sklearn import datasets, svm
>>> digits = datasets.load_digits()
>>> X_digits = digits.data
>>> y_digits = digits.target
>>> svc = svm.SVC(C=1, kernel='linear')
>>> svc.fit(X_digits[:-100], y_digits[:-100]).score(X_digits[-100:], y_digits[-100:])
0.98
```

To get a better measure of prediction accuracy (which we can use as a
proxy for goodness of fit of the model), we can successively split the
data in *folds* that we use for training and testing:

```
>>> import numpy as np
>>> X_folds = np.array_split(X_digits, 3)
>>> y_folds = np.array_split(y_digits, 3)
>>> scores = list()
>>> for k in range(3):
... # We use 'list' to copy, in order to 'pop' later on
... X_train = list(X_folds)
... X_test = X_train.pop(k)
... X_train = np.concatenate(X_train)
... y_train = list(y_folds)
... y_test = y_train.pop(k)
... y_train = np.concatenate(y_train)
... scores.append(svc.fit(X_train, y_train).score(X_test, y_test))
>>> print(scores)
[0.934..., 0.956..., 0.939...]
```

This is called a `KFold`

cross-validation.

## Cross-validation generators¶

Scikit-learn has a collection of classes which can be used to generate lists of train/test indices for popular cross-validation strategies.

They expose a `split`

method which accepts the input
dataset to be split and yields the train/test set indices for each iteration
of the chosen cross-validation strategy.

This example shows an example usage of the `split`

method.

```
>>> from sklearn.model_selection import KFold, cross_val_score
>>> X = ["a", "a", "b", "c", "c", "c"]
>>> k_fold = KFold(n_splits=3)
>>> for train_indices, test_indices in k_fold.split(X):
... print('Train: %s | test: %s' % (train_indices, test_indices))
Train: [2 3 4 5] | test: [0 1]
Train: [0 1 4 5] | test: [2 3]
Train: [0 1 2 3] | test: [4 5]
```

The cross-validation can then be performed easily:

```
>>> [svc.fit(X_digits[train], y_digits[train]).score(X_digits[test], y_digits[test])
... for train, test in k_fold.split(X_digits)]
[0.934..., 0.956..., 0.939...]
```

The cross-validation score can be directly calculated using the
`cross_val_score`

helper. Given an estimator, the cross-validation object
and the input dataset, the `cross_val_score`

splits the data repeatedly into
a training and a testing set, trains the estimator using the training set and
computes the scores based on the testing set for each iteration of cross-validation.

By default the estimator’s `score`

method is used to compute the individual scores.

Refer the metrics module to learn more on the available scoring methods.

```
>>> cross_val_score(svc, X_digits, y_digits, cv=k_fold, n_jobs=-1)
array([0.93489149, 0.95659432, 0.93989983])
```

n_jobs=-1 means that the computation will be dispatched on all the CPUs of the computer.

Alternatively, the `scoring`

argument can be provided to specify an alternative
scoring method.

>>> cross_val_score(svc, X_digits, y_digits, cv=k_fold, ... scoring='precision_macro') array([0.93969761, 0.95911415, 0.94041254])

Cross-validation generators

`KFold` (n_splits, shuffle, random_state) |
`StratifiedKFold` (n_splits, shuffle, random_state) |
`GroupKFold` (n_splits) |

Splits it into K folds, trains on K-1 and then tests on the left-out. | Same as K-Fold but preserves the class distribution within each fold. | Ensures that the same group is not in both testing and training sets. |

`ShuffleSplit` (n_splits, test_size, train_size, random_state) |
`StratifiedShuffleSplit` |
`GroupShuffleSplit` |

Generates train/test indices based on random permutation. | Same as shuffle split but preserves the class distribution within each iteration. | Ensures that the same group is not in both testing and training sets. |

`LeaveOneGroupOut` () |
`LeavePGroupsOut` (n_groups) |
`LeaveOneOut` () |

Takes a group array to group observations. | Leave P groups out. | Leave one observation out. |

`LeavePOut` (p) |
`PredefinedSplit` |

Leave P observations out. | Generates train/test indices based on predefined splits. |

**Exercise**

On the digits dataset, plot the cross-validation score of a `SVC`

estimator with an linear kernel as a function of parameter `C`

(use a
logarithmic grid of points, from 1 to 10).

```
import numpy as np
from sklearn.model_selection import cross_val_score
from sklearn import datasets, svm
digits = datasets.load_digits()
X = digits.data
y = digits.target
svc = svm.SVC(kernel='linear')
C_s = np.logspace(-10, 0, 10)
```

## Grid-search and cross-validated estimators¶

### Grid-search¶

scikit-learn provides an object that, given data, computes the score during the fit of an estimator on a parameter grid and chooses the parameters to maximize the cross-validation score. This object takes an estimator during the construction and exposes an estimator API:

```
>>> from sklearn.model_selection import GridSearchCV, cross_val_score
>>> Cs = np.logspace(-6, -1, 10)
>>> clf = GridSearchCV(estimator=svc, param_grid=dict(C=Cs),
... n_jobs=-1)
>>> clf.fit(X_digits[:1000], y_digits[:1000])
GridSearchCV(cv=None,...
>>> clf.best_score_
0.925...
>>> clf.best_estimator_.C
0.0077...
>>> # Prediction performance on test set is not as good as on train set
>>> clf.score(X_digits[1000:], y_digits[1000:])
0.943...
```

By default, the `GridSearchCV`

uses a 3-fold cross-validation. However,
if it detects that a classifier is passed, rather than a regressor, it uses
a stratified 3-fold.

Nested cross-validation

```
>>> cross_val_score(clf, X_digits, y_digits)
...
array([0.938..., 0.963..., 0.944...])
```

Two cross-validation loops are performed in parallel: one by the
`GridSearchCV`

estimator to set `gamma`

and the other one by
`cross_val_score`

to measure the prediction performance of the
estimator. The resulting scores are unbiased estimates of the
prediction score on new data.

Warning

You cannot nest objects with parallel computing (`n_jobs`

different
than 1).

### Cross-validated estimators¶

Cross-validation to set a parameter can be done more efficiently on an algorithm-by-algorithm basis. This is why, for certain estimators, scikit-learn exposes Cross-validation: evaluating estimator performance estimators that set their parameter automatically by cross-validation:

```
>>> from sklearn import linear_model, datasets
>>> lasso = linear_model.LassoCV()
>>> diabetes = datasets.load_diabetes()
>>> X_diabetes = diabetes.data
>>> y_diabetes = diabetes.target
>>> lasso.fit(X_diabetes, y_diabetes)
LassoCV(alphas=None, copy_X=True, cv=None, eps=0.001, fit_intercept=True,
max_iter=1000, n_alphas=100, n_jobs=1, normalize=False, positive=False,
precompute='auto', random_state=None, selection='cyclic', tol=0.0001,
verbose=False)
>>> # The estimator chose automatically its lambda:
>>> lasso.alpha_
0.01229...
```

These estimators are called similarly to their counterparts, with ‘CV’ appended to their name.

**Exercise**

On the diabetes dataset, find the optimal regularization parameter alpha.

**Bonus**: How much can you trust the selection of alpha?

```
from sklearn import datasets
from sklearn.linear_model import LassoCV
from sklearn.linear_model import Lasso
from sklearn.model_selection import KFold
from sklearn.model_selection import GridSearchCV
diabetes = datasets.load_diabetes()
```