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Release Highlights for scikit-learn 1.7#
We are pleased to announce the release of scikit-learn 1.7! Many bug fixes and improvements were added, as well as some key new features. Below we detail the highlights of this release. For an exhaustive list of all the changes, please refer to the release notes.
To install the latest version (with pip):
pip install --upgrade scikit-learn
or with conda:
conda install -c conda-forge scikit-learn
Improved estimator’s HTML representation#
The HTML representation of estimators now includes a section containing the list of
parameters and their values. Non-default parameters are highlighted in orange. A copy
button is also available to copy the “fully-qualified” parameter name without the
need to call the get_params method. It is particularly useful when defining a
parameter grid for a grid-search or a randomized-search with a complex pipeline.
See the example below and click on the different estimator’s blocks to see the improved HTML representation.
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
model = make_pipeline(StandardScaler(with_std=False), LogisticRegression(C=2.0))
model
Custom validation set for histogram-based Gradient Boosting estimators#
The ensemble.HistGradientBoostingClassifier and
ensemble.HistGradientBoostingRegressor now support directly passing a custom
validation set for early stopping to the fit method, using the X_val, y_val, and
sample_weight_val parameters.
In a pipeline.Pipeline, the validation set X_val can be transformed along
with X using the transform_input parameter.
import sklearn
from sklearn.datasets import make_classification
from sklearn.ensemble import HistGradientBoostingClassifier
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
sklearn.set_config(enable_metadata_routing=True)
X, y = make_classification(random_state=0)
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=0)
clf = HistGradientBoostingClassifier()
clf.set_fit_request(X_val=True, y_val=True)
model = Pipeline([("sc", StandardScaler()), ("clf", clf)], transform_input=["X_val"])
model.fit(X, y, X_val=X_val, y_val=y_val)
Plotting ROC curves from cross-validation results#
The class metrics.RocCurveDisplay has a new class method from_cv_results
that allows to easily plot multiple ROC curves from the results of
model_selection.cross_validate.
from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import RocCurveDisplay
from sklearn.model_selection import cross_validate
X, y = make_classification(n_samples=150, random_state=0)
clf = LogisticRegression(random_state=0)
cv_results = cross_validate(clf, X, y, cv=5, return_estimator=True, return_indices=True)
_ = RocCurveDisplay.from_cv_results(cv_results, X, y)
Array API support#
Several functions have been updated to support array API compatible inputs since
version 1.6, especially metrics from the sklearn.metrics module.
In addition, it is no longer required to install the array-api-compat package to use
the experimental array API support in scikit-learn.
Please refer to the array API support page for instructions to use scikit-learn with array API compatible libraries such as PyTorch or CuPy.
Improved API consistency of Multi-layer Perceptron#
The neural_network.MLPRegressor has a new parameter loss and now supports
the “poisson” loss in addition to the default “squared_error” loss.
Moreover, the neural_network.MLPClassifier and
neural_network.MLPRegressor estimators now support sample weights.
These improvements have been made to improve the consistency of these estimators
with regard to the other estimators in scikit-learn.
Migration toward sparse arrays#
In order to prepare SciPy migration from sparse matrices to sparse arrays, all scikit-learn estimators that accept sparse matrices as input now also accept sparse arrays.
Total running time of the script: (0 minutes 0.206 seconds)
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