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skore is a Python library to evaluate and get insights from your predictive models.
We structure and store your experiments so that you can easily retrieve them later.

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Reports for your Experiments

Evaluate one or several estimators, with a single train-test split or by cross-validation and get a structured report with the evaluate function.

It returns an EstimatorReport, a CrossValidationReport, or a ComparisonReport, all with the same API to discover all facets of your predictive models while experimenting.

Given some data df: (expand for full code)

from skrub.datasets import fetch_california_housing
dataset = fetch_california_housing()
df = dataset.X
y = dataset.y

import sklearn
import skore
import skrub

model = skrub.tabular_pipeline(sklearn.linear_model.Ridge())
report_ridge = skore.evaluate(model, df, y, splitter=5)
report_ridge

Pipeline(steps=[('tablevectorizer',
                 TableVectorizer(datetime=DatetimeEncoder(periodic_encoding='spline'))),
                ('simpleimputer', SimpleImputer(add_indicator=True)),
                ('squashingscaler', SquashingScaler(max_absolute_value=5)),
                ('ridge', Ridge())])

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Get Insights that Matter

Quickly generate beautiful visualizations with display.plot()

error = report_ridge.metrics.prediction_error()
error.plot(kind="actual_vs_predicted")

and access the raw data with display.frame()

report_ridge.metrics.summarize().frame(
    aggregate=None
)

	Ridge
	Split #0	Split #1	Split #2	Split #3	Split #4
Metric
Score	0.592291	0.573757	0.640446	0.588800	0.702893
R²	0.592291	0.573757	0.640446	0.588800	0.702893
RMSE	0.661809	0.706360	0.719187	0.694560	0.657976
MAE	0.528334	0.495339	0.526417	0.522271	0.479352
MAPE	0.452064	0.226631	0.265538	0.336301	0.267484
Fit time (s)	0.059231	0.047790	0.043364	0.044963	0.043547
Predict time (s)	0.013659	0.013046	0.013417	0.013077	0.012912

Store and Retrieve your Reports, Locally or on Skore Hub

Project stores and retrieves your reports so you can revisit insights or compare with new experiments later.

import numpy as np

project = skore.Project(
    name="adult_census_survey", mode="local"
)
for alpha in np.logspace(-5, 5, 11):
    model = skrub.tabular_pipeline(
        sklearn.linear_model.Ridge(alpha=alpha)
    )
    project.put(
        f"ridge-{alpha:g}",
        skore.evaluate(model, df, y, splitter=3),
    )

Store everything locally on disk, or on Skore Hub to enhance your exploration and search for the impactful insights.

See a live example on Skore Hub →

Our Community

The skore project is driven by Probabl together with a world-wide community of contributors. Here we display a randomly selected group of 30 contributors.

Try it yourself!

Ready to write less code, focus on what matters, and store your experiments to retrieve them later? Dive into skore now and be part of our growing community!

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Metric	mean	std
Score	0.619637	0.052840
R²	0.619637	0.052840
RMSE	0.687978	0.027112
MAE	0.510343	0.021851
MAPE	0.309603	0.088893
Fit time (s)	0.047779	0.006642
Predict time (s)	0.013222	0.000307

	steps steps: list of tuples List of (name of step, estimator) tuples that are to be chained in sequential order. To be compatible with the scikit-learn API, all steps must define `fit`. All non-last steps must also define `transform`. See :ref:`Combining Estimators <combining_estimators>` for more details.	[('tablevectorizer', ...), ('simpleimputer', ...), ...]
	transform_input transform_input: list of str, default=None The names of the :term:`metadata` parameters that should be transformed by the pipeline before passing it to the step consuming it. This enables transforming some input arguments to ``fit`` (other than ``X``) to be transformed by the steps of the pipeline up to the step which requires them. Requirement is defined via :ref:`metadata routing <metadata_routing>`. For instance, this can be used to pass a validation set through the pipeline. You can only set this if metadata routing is enabled, which you can enable using ``sklearn.set_config(enable_metadata_routing=True)``. .. versionadded:: 1.6	None
	memory memory: str or object with the joblib.Memory interface, default=None Used to cache the fitted transformers of the pipeline. The last step will never be cached, even if it is a transformer. By default, no caching is performed. If a string is given, it is the path to the caching directory. Enabling caching triggers a clone of the transformers before fitting. Therefore, the transformer instance given to the pipeline cannot be inspected directly. Use the attribute ``named_steps`` or ``steps`` to inspect estimators within the pipeline. Caching the transformers is advantageous when fitting is time consuming. See :ref:`sphx_glr_auto_examples_neighbors_plot_caching_nearest_neighbors.py` for an example on how to enable caching.	None
	verbose verbose: bool, default=False If True, the time elapsed while fitting each step will be printed as it is completed.	False

	low_cardinality	OneHotEncoder..._output=False)
	high_cardinality	StringEncoder()
	numeric	PassThrough()
	datetime	DatetimeEncod...ding='spline')
	cardinality_threshold	40
	specific_transformers	()
	drop_null_fraction	1.0
	drop_if_constant	False
	drop_if_unique	False
	datetime_format	None
	null_strings	None
	n_jobs	None

	periodic_encoding	'spline'
	resolution	'hour'
	add_weekday	False
	add_total_seconds	True
	add_day_of_year	False

	drop drop: {'first', 'if_binary'} or an array-like of shape (n_features,), default=None Specifies a methodology to use to drop one of the categories per feature. This is useful in situations where perfectly collinear features cause problems, such as when feeding the resulting data into an unregularized linear regression model. However, dropping one category breaks the symmetry of the original representation and can therefore induce a bias in downstream models, for instance for penalized linear classification or regression models. - None : retain all features (the default). - 'first' : drop the first category in each feature. If only one category is present, the feature will be dropped entirely. - 'if_binary' : drop the first category in each feature with two categories. Features with 1 or more than 2 categories are left intact. - array : ``drop[i]`` is the category in feature ``X[:, i]`` that should be dropped. When `max_categories` or `min_frequency` is configured to group infrequent categories, the dropping behavior is handled after the grouping. .. versionadded:: 0.21 The parameter `drop` was added in 0.21. .. versionchanged:: 0.23 The option `drop='if_binary'` was added in 0.23. .. versionchanged:: 1.1 Support for dropping infrequent categories.	'if_binary'
	sparse_output sparse_output: bool, default=True When ``True``, it returns a SciPy sparse matrix/array in "Compressed Sparse Row" (CSR) format. .. versionadded:: 1.2 `sparse` was renamed to `sparse_output`	False
	dtype dtype: number type, default=np.float64 Desired dtype of output.	'float32'
	handle_unknown handle_unknown: {'error', 'ignore', 'infrequent_if_exist', 'warn'}, default='error' Specifies the way unknown categories are handled during :meth:`transform`. - 'error' : Raise an error if an unknown category is present during transform. - 'ignore' : When an unknown category is encountered during transform, the resulting one-hot encoded columns for this feature will be all zeros. In the inverse transform, an unknown category will be denoted as None. - 'infrequent_if_exist' : When an unknown category is encountered during transform, the resulting one-hot encoded columns for this feature will map to the infrequent category if it exists. The infrequent category will be mapped to the last position in the encoding. During inverse transform, an unknown category will be mapped to the category denoted `'infrequent'` if it exists. If the `'infrequent'` category does not exist, then :meth:`transform` and :meth:`inverse_transform` will handle an unknown category as with `handle_unknown='ignore'`. Infrequent categories exist based on `min_frequency` and `max_categories`. Read more in the :ref:`User Guide <encoder_infrequent_categories>`. - 'warn' : When an unknown category is encountered during transform a warning is issued, and the encoding then proceeds as described for `handle_unknown="infrequent_if_exist"`. .. versionchanged:: 1.1 `'infrequent_if_exist'` was added to automatically handle unknown categories and infrequent categories. .. versionadded:: 1.6 The option `"warn"` was added in 1.6.	'ignore'
	categories categories: 'auto' or a list of array-like, default='auto' Categories (unique values) per feature: - 'auto' : Determine categories automatically from the training data. - list : ``categories[i]`` holds the categories expected in the ith column. The passed categories should not mix strings and numeric values within a single feature, and should be sorted in case of numeric values. The used categories can be found in the ``categories_`` attribute. .. versionadded:: 0.20	'auto'
	min_frequency min_frequency: int or float, default=None Specifies the minimum frequency below which a category will be considered infrequent. - If `int`, categories with a smaller cardinality will be considered infrequent. - If `float`, categories with a smaller cardinality than `min_frequency * n_samples` will be considered infrequent. .. versionadded:: 1.1 Read more in the :ref:`User Guide <encoder_infrequent_categories>`.	None
	max_categories max_categories: int, default=None Specifies an upper limit to the number of output features for each input feature when considering infrequent categories. If there are infrequent categories, `max_categories` includes the category representing the infrequent categories along with the frequent categories. If `None`, there is no limit to the number of output features. .. versionadded:: 1.1 Read more in the :ref:`User Guide <encoder_infrequent_categories>`.	None
	feature_name_combiner feature_name_combiner: "concat" or callable, default="concat" Callable with signature `def callable(input_feature, category)` that returns a string. This is used to create feature names to be returned by :meth:`get_feature_names_out`. `"concat"` concatenates encoded feature name and category with `feature + "_" + str(category)`.E.g. feature X with values 1, 6, 7 create feature names `X_1, X_6, X_7`. .. versionadded:: 1.3	'concat'

skore

Track your Data Science

Reports for your Experiments

MedInc

HouseAge

AveRooms

AveBedrms

Population

AveOccup

Latitude

Longitude

MedHouseVal

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Get Insights that Matter

Store and Retrieve your Reports, Locally or on Skore Hub

Our Community

Try it yourself!

	n_components	30
	vectorizer	'tfidf'
	ngram_range	(3, ...)
	analyzer	'char_wb'
	stop_words	None
	random_state	None
	vocabulary	None

	add_indicator add_indicator: bool, default=False If True, a :class:`MissingIndicator` transform will stack onto output of the imputer's transform. This allows a predictive estimator to account for missingness despite imputation. If a feature has no missing values at fit/train time, the feature won't appear on the missing indicator even if there are missing values at transform/test time.	True
	missing_values missing_values: int, float, str, np.nan, None or pandas.NA, default=np.nan The placeholder for the missing values. All occurrences of `missing_values` will be imputed. For pandas' dataframes with nullable integer dtypes with missing values, `missing_values` can be set to either `np.nan` or `pd.NA`.	nan
	strategy strategy: str or Callable, default='mean' The imputation strategy. - If "mean", then replace missing values using the mean along each column. Can only be used with numeric data. - If "median", then replace missing values using the median along each column. Can only be used with numeric data. - If "most_frequent", then replace missing using the most frequent value along each column. Can be used with strings or numeric data. If there is more than one such value, only the smallest is returned. - If "constant", then replace missing values with fill_value. Can be used with strings or numeric data. - If an instance of Callable, then replace missing values using the scalar statistic returned by running the callable over a dense 1d array containing non-missing values of each column. .. versionadded:: 0.20 strategy="constant" for fixed value imputation. .. versionadded:: 1.5 strategy=callable for custom value imputation.	'mean'
	fill_value fill_value: str or numerical value, default=None When strategy == "constant", `fill_value` is used to replace all occurrences of missing_values. For string or object data types, `fill_value` must be a string. If `None`, `fill_value` will be 0 when imputing numerical data and "missing_value" for strings or object data types.	None
	copy copy: bool, default=True If True, a copy of X will be created. If False, imputation will be done in-place whenever possible. Note that, in the following cases, a new copy will always be made, even if `copy=False`: - If `X` is not an array of floating values; - If `X` is encoded as a CSR matrix; - If `add_indicator=True`.	True
	keep_empty_features keep_empty_features: bool, default=False If True, features that consist exclusively of missing values when `fit` is called are returned in results when `transform` is called. The imputed value is always `0` except when `strategy="constant"` in which case `fill_value` will be used instead. .. versionadded:: 1.2	False

	alpha alpha: float or array-like of shape (n_targets,), default=1.0 Constant that multiplies the L2 term, controlling regularization strength. `alpha` must be a non-negative float i.e. in `[0, inf)`. When `alpha = 0`, the objective is equivalent to ordinary least squares, solved by the :class:`LinearRegression` object. For numerical reasons, using `alpha = 0` with the `Ridge` object is not advised. Instead, you should use the :class:`LinearRegression` object. If an array is passed, penalties are assumed to be specific to the targets. Hence they must correspond in number. See :ref:`sphx_glr_auto_examples_linear_model_plot_ridge_coeffs.py` for an illustration of the effect of alpha on the model coefficients.	1.0
	fit_intercept fit_intercept: bool, default=True Whether to fit the intercept for this model. If set to false, no intercept will be used in calculations (i.e. ``X`` and ``y`` are expected to be centered).	True
	copy_X copy_X: bool, default=True If True, X will be copied; else, it may be overwritten.	True
	max_iter max_iter: int, default=None Maximum number of iterations for conjugate gradient solver. For 'sparse_cg' and 'lsqr' solvers, the default value is determined by scipy.sparse.linalg. For 'sag' solver, the default value is 1000. For 'lbfgs' solver, the default value is 15000.	None
	tol tol: float, default=1e-4 The precision of the solution (`coef_`) is determined by `tol` which specifies a different convergence criterion for each solver: - 'svd': `tol` has no impact. - 'cholesky': `tol` has no impact. - 'sparse_cg': norm of residuals smaller than `tol`. - 'lsqr': `tol` is set as atol and btol of scipy.sparse.linalg.lsqr, which control the norm of the residual vector in terms of the norms of matrix and coefficients. - 'sag' and 'saga': relative change of coef smaller than `tol`. - 'lbfgs': maximum of the absolute (projected) gradient=max\|residuals\| smaller than `tol`. .. versionchanged:: 1.2 Default value changed from 1e-3 to 1e-4 for consistency with other linear models.	0.0001
	solver solver: {'auto', 'svd', 'cholesky', 'lsqr', 'sparse_cg', 'sag', 'saga', 'lbfgs'}, default='auto' Solver to use in the computational routines: - 'auto' chooses the solver automatically based on the type of data. - 'svd' uses a Singular Value Decomposition of X to compute the Ridge coefficients. It is the most stable solver, in particular more stable for singular matrices than 'cholesky' at the cost of being slower. - 'cholesky' uses the standard :func:`scipy.linalg.solve` function to obtain a closed-form solution. - 'sparse_cg' uses the conjugate gradient solver as found in :func:`scipy.sparse.linalg.cg`. As an iterative algorithm, this solver is more appropriate than 'cholesky' for large-scale data (possibility to set `tol` and `max_iter`). - 'lsqr' uses the dedicated regularized least-squares routine :func:`scipy.sparse.linalg.lsqr`. It is the fastest and uses an iterative procedure. - 'sag' uses a Stochastic Average Gradient descent, and 'saga' uses its improved, unbiased version named SAGA. Both methods also use an iterative procedure, and are often faster than other solvers when both n_samples and n_features are large. Note that 'sag' and 'saga' fast convergence is only guaranteed on features with approximately the same scale. You can preprocess the data with a scaler from :mod:`sklearn.preprocessing`. - 'lbfgs' uses L-BFGS-B algorithm implemented in :func:`scipy.optimize.minimize`. It can be used only when `positive` is True. All solvers except 'svd' support both dense and sparse data. However, only 'lsqr', 'sag', 'sparse_cg', and 'lbfgs' support sparse input when `fit_intercept` is True. .. versionadded:: 0.17 Stochastic Average Gradient descent solver. .. versionadded:: 0.19 SAGA solver.	'auto'
	positive positive: bool, default=False When set to ``True``, forces the coefficients to be positive. Only 'lbfgs' solver is supported in this case.	False
	random_state random_state: int, RandomState instance, default=None Used when ``solver`` == 'sag' or 'saga' to shuffle the data. See :term:`Glossary <random_state>` for details. .. versionadded:: 0.17 `random_state` to support Stochastic Average Gradient.	None

Column	Column name	dtype	Is sorted	Unique values	Mean	Std	Min	Median	Max
0	MedInc	Float64DType	False	12928 (62.6%)	3.87	1.90	0.500	3.53	15.0
1	HouseAge	Float64DType	False	52 (0.3%)	28.6	12.6	1.00	29.0	52.0
2	AveRooms	Float64DType	False	19392 (94.0%)	5.43	2.47	0.846	5.23	142.
3	AveBedrms	Float64DType	False	14233 (69.0%)	1.10	0.474	0.333	1.05	34.1
4	Population	Float64DType	False	3888 (18.8%)	1.43e+03	1.13e+03	3.00	1.17e+03	3.57e+04
5	AveOccup	Float64DType	False	18841 (91.3%)	3.07	10.4	0.692	2.82	1.24e+03
6	Latitude	Float64DType	False	862 (4.2%)	35.6	2.14	32.5	34.3	42.0
7	Longitude	Float64DType	False	844 (4.1%)	-120.	2.00	-124.	-118.	-114.
8	MedHouseVal	Float64DType	False	3842 (18.6%)	2.07	1.15	0.150	1.80	5.00

ID	Key	Date	Learner	Dataset	Report type
15022214391950218116085557122233942433	ridge-1e-05	2026-06-15T18:45:10.194543+00:00	Ridge	2f75b4948a1fccb9113e39ed79aa5e52	cross-validation
4505765511158343296825270866681662414	ridge-0.0001	2026-06-15T18:45:10.612043+00:00	Ridge	2f75b4948a1fccb9113e39ed79aa5e52	cross-validation
304645107036772643826226891013377174676	ridge-0.001	2026-06-15T18:45:10.872459+00:00	Ridge	2f75b4948a1fccb9113e39ed79aa5e52	cross-validation
98840678236995058344630350806185733040	ridge-0.01	2026-06-15T18:45:11.130254+00:00	Ridge	2f75b4948a1fccb9113e39ed79aa5e52	cross-validation
239480621306713955790559209635150324522	ridge-0.1	2026-06-15T18:45:11.389664+00:00	Ridge	2f75b4948a1fccb9113e39ed79aa5e52	cross-validation
64488403228614870825466112331667527988	ridge-1	2026-06-15T18:45:11.648026+00:00	Ridge	2f75b4948a1fccb9113e39ed79aa5e52	cross-validation
106217960617478543957293840892532585344	ridge-10	2026-06-15T18:45:11.906551+00:00	Ridge	2f75b4948a1fccb9113e39ed79aa5e52	cross-validation
265690874089788107331511896854338300226	ridge-100	2026-06-15T18:45:12.166579+00:00	Ridge	2f75b4948a1fccb9113e39ed79aa5e52	cross-validation
333706368217845048392803242139263410341	ridge-1000	2026-06-15T18:45:12.425958+00:00	Ridge	2f75b4948a1fccb9113e39ed79aa5e52	cross-validation
130115489674265762972961660073780856654	ridge-10000	2026-06-15T18:45:12.686474+00:00	Ridge	2f75b4948a1fccb9113e39ed79aa5e52	cross-validation
127333736223497430512243076453933591815	ridge-100000	2026-06-15T18:45:12.945050+00:00	Ridge	2f75b4948a1fccb9113e39ed79aa5e52	cross-validation

	max_absolute_value	5
	quantile_range	(25.0, ...)

	MedInc	HouseAge	AveRooms	AveBedrms	Population	AveOccup	Latitude	Longitude	MedHouseVal
	MedInc	HouseAge	AveRooms	AveBedrms	Population	AveOccup	Latitude	Longitude	MedHouseVal
0	8.33	41.0	6.98	1.02	322.	2.56	37.9	-122.	4.53
1	8.30	21.0	6.24	0.972	2.40e+03	2.11	37.9	-122.	3.58
2	7.26	52.0	8.29	1.07	496.	2.80	37.9	-122.	3.52
3	5.64	52.0	5.82	1.07	558.	2.55	37.9	-122.	3.41
4	3.85	52.0	6.28	1.08	565.	2.18	37.9	-122.	3.42

20,635	1.56	25.0	5.05	1.13	845.	2.56	39.5	-121.	0.781
20,636	2.56	18.0	6.11	1.32	356.	3.12	39.5	-121.	0.771
20,637	1.70	17.0	5.21	1.12	1.01e+03	2.33	39.4	-121.	0.923
20,638	1.87	18.0	5.33	1.17	741.	2.12	39.4	-121.	0.847
20,639	2.39	16.0	5.25	1.16	1.39e+03	2.62	39.4	-121.	0.894