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scikit-learn 1.3.2
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# Release Highlights for scikit-learn 1.2

We are pleased to announce the release of scikit-learn 1.2! Many bug fixes and improvements were added, as well as some new key features. We detail below a few of the major features 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

## Pandas output with `set_output` API

scikit-learn’s transformers now support pandas output with the `set_output` API. To learn more about the `set_output` API see the example: Introducing the set_output API and # this video, pandas DataFrame output for scikit-learn transformers (some examples).

    import numpy as np
    from sklearn.datasets import load_iris
    from sklearn.preprocessing import StandardScaler, KBinsDiscretizer
    from sklearn.compose import ColumnTransformer

    X, y = load_iris(as_frame=True, return_X_y=True)
    sepal_cols = ["sepal length (cm)", "sepal width (cm)"]
    petal_cols = ["petal length (cm)", "petal width (cm)"]

    preprocessor = ColumnTransformer(
        [
            ("scaler", StandardScaler(), sepal_cols),
            ("kbin", KBinsDiscretizer(encode="ordinal"), petal_cols),
        ],
        verbose_feature_names_out=False,
    ).set_output(transform="pandas")

    X_out = preprocessor.fit_transform(X)
    X_out.sample(n=5, random_state=0)

| sepal length (cm) | sepal width (cm) | petal length (cm) | petal width (cm)
--- | --- | --- | --- | ---
114 | -0.052506 | -0.592373 | 3.0 | 4.0
62 | 0.189830 | -1.973554 | 2.0 | 1.0
33 | -0.416010 | 2.630382 | 0.0 | 1.0
107 | 1.765012 | -0.362176 | 4.0 | 3.0
7 | -1.021849 | 0.788808 | 1.0 | 1.0

## Interaction constraints in Histogram-based Gradient Boosting Trees

`HistGradientBoostingRegressor` and `HistGradientBoostingClassifier` now supports interaction constraints with the `interaction_cst` parameter. For details, see the User Guide. In the following example, features are not allowed to interact.

    from sklearn.datasets import load_diabetes
    from sklearn.ensemble import HistGradientBoostingRegressor

    X, y = load_diabetes(return_X_y=True, as_frame=True)

    hist_no_interact = HistGradientBoostingRegressor(
        interaction_cst=[[i] for i in range(X.shape[1])], random_state=0
    )
    hist_no_interact.fit(X, y)

    HistGradientBoostingRegressor(interaction_cst=[[0], [1], [2], [3], [4], [5],
                                                   [6], [7], [8], [9]],
                                  random_state=0)

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HistGradientBoostingRegressor

    HistGradientBoostingRegressor(interaction_cst=[[0], [1], [2], [3], [4], [5],
                                                   [6], [7], [8], [9]],
                                  random_state=0)

## New and enhanced displays

`PredictionErrorDisplay` provides a way to analyze regression models in a qualitative manner.

    import matplotlib.pyplot as plt
    from sklearn.metrics import PredictionErrorDisplay

    fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(12, 5))
    _ = PredictionErrorDisplay.from_estimator(
        hist_no_interact, X, y, kind="actual_vs_predicted", ax=axs[0]
    )
    _ = PredictionErrorDisplay.from_estimator(
        hist_no_interact, X, y, kind="residual_vs_predicted", ax=axs[1]
    )

`LearningCurveDisplay` is now available to plot results from `learning_curve`.

    from sklearn.model_selection import LearningCurveDisplay

    _ = LearningCurveDisplay.from_estimator(
        hist_no_interact, X, y, cv=5, n_jobs=2, train_sizes=np.linspace(0.1, 1, 5)
    )

`PartialDependenceDisplay` exposes a new parameter `categorical_features` to display partial dependence for categorical features using bar plots and heatmaps.

    from sklearn.datasets import fetch_openml

    X, y = fetch_openml(
        "titanic", version=1, as_frame=True, return_X_y=True, parser="pandas"
    )
    X = X.select_dtypes(["number", "category"]).drop(columns=["body"])

    from sklearn.preprocessing import OrdinalEncoder
    from sklearn.pipeline import make_pipeline

    categorical_features = ["pclass", "sex", "embarked"]
    model = make_pipeline(
        ColumnTransformer(
            transformers=[("cat", OrdinalEncoder(), categorical_features)],
            remainder="passthrough",
        ),
        HistGradientBoostingRegressor(random_state=0),
    ).fit(X, y)

    from sklearn.inspection import PartialDependenceDisplay

    fig, ax = plt.subplots(figsize=(14, 4), constrained_layout=True)
    _ = PartialDependenceDisplay.from_estimator(
        model,
        X,
        features=["age", "sex", ("pclass", "sex")],
        categorical_features=categorical_features,
        ax=ax,
    )

## Faster parser in `fetch_openml`

`fetch_openml` now supports a new `"pandas"` parser that is more memory and CPU efficient. In v1.4, the default will change to `parser="auto"` which will automatically use the `"pandas"` parser for dense data and `"liac-arff"` for sparse data.

    X, y = fetch_openml(
        "titanic", version=1, as_frame=True, return_X_y=True, parser="pandas"
    )
    X.head()

| pclass | name | sex | age | sibsp | parch | ticket | fare | cabin | embarked | boat | body | home.dest
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
0 | 1 | Allen, Miss. Elisabeth Walton | female | 29.0000 | 0 | 0 | 24160 | 211.3375 | B5 | S | 2 | NaN | St Louis, MO
1 | 1 | Allison, Master. Hudson Trevor | male | 0.9167 | 1 | 2 | 113781 | 151.5500 | C22 C26 | S | 11 | NaN | Montreal, PQ / Chesterville, ON
2 | 1 | Allison, Miss. Helen Loraine | female | 2.0000 | 1 | 2 | 113781 | 151.5500 | C22 C26 | S | NaN | NaN | Montreal, PQ / Chesterville, ON
3 | 1 | Allison, Mr. Hudson Joshua Creighton | male | 30.0000 | 1 | 2 | 113781 | 151.5500 | C22 C26 | S | NaN | 135.0 | Montreal, PQ / Chesterville, ON
4 | 1 | Allison, Mrs. Hudson J C (Bessie Waldo Daniels) | female | 25.0000 | 1 | 2 | 113781 | 151.5500 | C22 C26 | S | NaN | NaN | Montreal, PQ / Chesterville, ON

## Experimental Array API support in `LinearDiscriminantAnalysis`

Experimental support for the Array API specification was added to `LinearDiscriminantAnalysis`. The estimator can now run on any Array API compliant libraries such as CuPy, a GPU-accelerated array library. For details, see the User Guide.

## Improved efficiency of many estimators

In version 1.1 the efficiency of many estimators relying on the computation of pairwise distances (essentially estimators related to clustering, manifold learning and neighbors search algorithms) was greatly improved for float64 dense input. Efficiency improvement especially were a reduced memory footprint and a much better scalability on multi-core machines. In version 1.2, the efficiency of these estimators was further improved for all combinations of dense and sparse inputs on float32 and float64 datasets, except the sparse-dense and dense-sparse combinations for the Euclidean and Squared Euclidean Distance metrics. A detailed list of the impacted estimators can be found in the changelog.

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