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Pipeline(steps=[('tablevectorizer', TableVectorizer(low_cardinality=ToCategorical())), ('histgradientboostingclassifier', HistGradientBoostingClassifier(early_stopping=True, max_iter=200, random_state=42))])

	gender	department	department_name	division	assignment_category	employee_position_title	date_first_hired	year_first_hired
4,953	F	FIN	Department of Finance	Payroll	Fulltime-Regular	Office Services Coordinator	05/30/2000	2,000
86	M	HRC	Office of Human Rights	Director's Office	Fulltime-Regular	Office Services Coordinator	10/14/2002	2,002
8,215	M	CAT	County Attorney's Office	Insurance Defense Litigation	Fulltime-Regular	Assistant County Attorney II	09/26/2011	2,011
4,534	F	CEC	Community Engagement Cluster	Gilchrist Center	Parttime-Regular	Program Specialist II	12/14/2015	2,015
5,538	M	COR	Correction and Rehabilitation	DS MCCF Unit 2 Security	Fulltime-Regular	Correctional Officer III (Corporal)	07/04/1995	1,995

7,466	M	FRS	Fire and Rescue Services	Station 2	Fulltime-Regular	Firefighter/Rescuer III	03/17/2008	2,008
8,072	F	HHS	Department of Health and Human Services	Income Supports	Fulltime-Regular	Income Assistance Program Specialist II	03/29/1999	1,999
7,986	M	FRS	Fire and Rescue Services	Station 19	Fulltime-Regular	Firefighter/Rescuer III	12/13/2004	2,004
255	M	DGS	Department of General Services	Fleet Automotive Heavy Equipment	Fulltime-Regular	Mechanic Technician II	06/26/2000	2,000
5,665	F	DLC	Department of Liquor Control	Kingsview	Parttime-Regular	Liquor Store Clerk I	06/29/2015	2,015

Column	Column name	dtype	Is sorted	Null values	Unique values	Mean	Std	Min	Median	Max
0	gender	ObjectDType	False	7 (0.2%)	2 (< 0.1%)
1	department	ObjectDType	False	0 (0.0%)	36 (1.2%)
2	department_name	ObjectDType	False	0 (0.0%)	36 (1.2%)
3	division	ObjectDType	False	0 (0.0%)	543 (18.1%)
4	assignment_category	ObjectDType	False	0 (0.0%)	2 (< 0.1%)
5	employee_position_title	ObjectDType	False	0 (0.0%)	322 (10.7%)
6	date_first_hired	ObjectDType	False	0 (0.0%)	1253 (41.8%)
7	year_first_hired	Int64DType	False	0 (0.0%)	46 (1.5%)	2.00e+03	9.27	1,968	2,005	2,016

Column 1	Column 2	Cramér's V
department	department_name	1.00
division	assignment_category	0.569
assignment_category	employee_position_title	0.502
division	employee_position_title	0.419
department_name	employee_position_title	0.414
department	employee_position_title	0.414
department	assignment_category	0.409
department_name	assignment_category	0.409
gender	department	0.380
gender	department_name	0.380
department	division	0.366
department_name	division	0.366
employee_position_title	date_first_hired	0.357
gender	employee_position_title	0.268
gender	division	0.252
gender	assignment_category	0.232
department	date_first_hired	0.149
department_name	date_first_hired	0.149
employee_position_title	year_first_hired	0.139
date_first_hired	year_first_hired	0.136
division	date_first_hired	0.125
gender	date_first_hired	0.0941
department	year_first_hired	0.0816
department_name	year_first_hired	0.0816
assignment_category	date_first_hired	0.0753
division	year_first_hired	0.0678
gender	year_first_hired	0.0646
assignment_category	year_first_hired	0.0637

	high_earner
4,953	0
86	0
8,215	1
4,534	0
5,538	1

7,466	0
8,072	1
7,986	1
255	1
5,665	0

Column	Column name	dtype	Is sorted	Null values	Unique values	Mean	Std	Min	Median	Max
0	high_earner	Int64DType	False	0 (0.0%)	2 (< 0.1%)	0.500	0.500	0	1	1

	GridSearchCV (train)	GridSearchCV (test)
score	-0.068969	-0.185339
accuracy	0.986667	0.935000
precision	0.985050	0.942373
recall	0.988333	0.926667
roc_auc	0.997617	0.978739
log_loss	0.068969	0.185339
brier_score	0.014416	0.052072
fit_time	9.694582	9.694582
predict_time	0.177579	0.078583

GridSearchCV (train)

GridSearchCV (test)

metric

score

-0.068969

-0.185339

accuracy

0.986667

0.935000

precision

0.985050

0.942373

recall

0.988333

0.926667

roc_auc

0.997617

0.978739

log_loss

0.068969

0.185339

brier_score

0.014416

0.052072

fit_time

9.694582

predict_time

0.177579

0.078583

metric	log_loss	roc_auc
max_iter_only (train)	0.068406	0.997792
max_iter_only (test)	0.188134	0.978422
two_point_edge_grid (train)	0.068969	0.997617
two_point_edge_grid (test)	0.185339	0.978739
padded_recommended_params (train)	0.078624	0.997178
padded_recommended_params (test)	0.195059	0.976078

metric

log_loss

roc_auc

max_iter_only (train)

0.068406

0.997792

max_iter_only (test)

0.188134

0.978422

two_point_edge_grid (train)

0.068969

0.997617

two_point_edge_grid (test)

0.185339

0.978739

padded_recommended_params (train)

0.078624

0.997178

padded_recommended_params (test)

0.195059

0.976078

	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', ...), ('histgradientboostingclassifier', ...)]
	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 low_cardinality: transformer, "passthrough" or "drop", default=OneHotEncoder instance The transformer for string or categorical columns with strictly fewer than ``cardinality_threshold`` unique values. By default, we use a :class:`~sklearn.preprocessing.OneHotEncoder` that ignores unknown categories and drop one of the transformed columns if the feature contains only 2 categories.	ToCategorical()
	high_cardinality high_cardinality: transformer, "passthrough" or "drop", default=StringEncoder instance The transformer for string or categorical columns with at least ``cardinality_threshold`` unique values. The default is a :class:`~skrub.StringEncoder` with 30 components (30 output columns for each input). .. versionchanged:: 0.6.0 The default ``high_cardinality`` encoder has been changed from :class:`~skrub.GapEncoder` to :class:`~skrub.StringEncoder`.	StringEncoder()
	numeric numeric: transformer, "passthrough" or "drop", default="passthrough" The transformer for numeric columns (floats, ints, booleans).	PassThrough()
	datetime datetime: transformer, "passthrough" or "drop", default=DatetimeEncoder instance The transformer for date and datetime columns. By default, we use a :class:`~skrub.DatetimeEncoder`.	DatetimeEncoder()
	cardinality_threshold cardinality_threshold: int, default=40 String and categorical columns with a number of unique values strictly smaller than this threshold are handled by the transformer ``low_cardinality``, the rest are handled by the transformer ``high_cardinality``.	40
	specific_transformers specific_transformers: list of (transformer, list of column names) pairs, default=() Override the categories above for the given columns and force using the specified transformer. This disables any preprocessing usually done by the TableVectorizer; the columns are passed to the transformer without any modification. A column is not allowed to appear twice in ``specific_transformers``. Consider wrapping the ``TableVectorizer`` in :class:`~skrub.ApplyToCols` to select or exclude specific columns from the processing. Alternatively, the :ref:`skrub Data Ops <user_guide_data_ops_index>` allows for more complex pre-processing.	()
	drop_null_fraction drop_null_fraction: float or None, default=1.0 Fraction of null above which the column is dropped. If `drop_null_fraction` is set to ``1.0``, the column is dropped if it contains only nulls or NaNs (this is the default behavior). If `drop_null_fraction` is a number in ``[0.0, 1.0)``, the column is dropped if the fraction of nulls is strictly larger than `drop_null_fraction`. If `drop_null_fraction` is ``None``, this selection is disabled: no columns are dropped based on the number of null values they contain.	1.0
	drop_if_constant drop_if_constant: bool, default=False If set to true, drop columns that contain a single unique value. Note that missing values are considered as one additional distinct value.	False
	drop_if_unique drop_if_unique: bool, default=False If set to true, drop columns that contain only unique values, i.e., the number of unique values is equal to the number of rows in the column. Numeric columns are never dropped. .. deprecated:: 0.9.0 This functionality can drop informative columns and is unlikely to be of use in practice. It is therefore deprecated and will be removed in a future version.	False
	datetime_format datetime_format: str, default=None The format to use when parsing dates. If None, the format is inferred.	None
	null_strings null_strings: str or sequence of str, default=None Additional strings to consider as null values, beyond the default list.	None
	n_jobs n_jobs: int, default=None Number of jobs to run in parallel. ``None`` means 1 unless in a joblib ``parallel_backend`` context. ``-1`` means using all processors.	None

	resolution resolution: str or None, default="hour" If a string, extract up to this resolution. Must be "year", "month", "day", "hour", "minute", "second", "microsecond", or "nanosecond". For example, ``resolution="day"`` generates the features "year", "month", and "day" only. If the input column contains dates with no time information, time features ("hour", "minute", … ) are never extracted. If ``None``, the features listed above are not extracted (but day of the week and total seconds may still be extracted, see below).	'hour'
	add_weekday add_weekday: bool, default=False Extract the day of the week as a numerical feature from 1 (Monday) to 7 (Sunday).	False
	add_total_seconds add_total_seconds: bool, default=True Add the total number of seconds since the Unix epoch (00:00:00 UTC on 1 January 1970).	True
	add_day_of_year add_day_of_year: bool, default=False Add the day of year (ordinal day) as an integer in the range 1 to 365 (or 366 in the case of leap years). January 1st will be day 1, December 31st will be day 365 on non-leap years.	False
	periodic_encoding periodic_encoding: 'circular', 'spline', or None, default=None Add periodic features with different granularities. Add periodic features using either trigonometric (``circular``) or ``spline`` encoding. If ``None``, no periodic encoding is applied.	None

	n_components n_components: int, default=30 Number of components to be used for the singular value decomposition (SVD). Must be a positive integer.	30
	vectorizer vectorizer: str, "tfidf" or "hashing", default="tfidf" Vectorizer to apply to the strings, either `tfidf` or `hashing` for scikit-learn TfidfVectorizer or HashingVectorizer respectively.	'tfidf'
	ngram_range ngram_range: tuple of (int, int) pairs, default=(3,4) The lower and upper boundary of the range of n-values for different n-grams to be extracted. All values of n such that min_n <= n <= max_n will be used. For example an ``ngram_range`` of ``(1, 1)`` means only unigrams, ``(1, 2)`` means unigrams and bigrams, and ``(2, 2)`` means only bigrams.	(3, ...)
	analyzer analyzer: str, "char", "word" or "char_wb", default="char_wb" Whether the feature should be made of word or character n-grams. Option ``char_wb`` creates character n-grams only from text inside word boundaries; n-grams at the edges of words are padded with space.	'char_wb'
	stop_words stop_words: {'english'}, list, default=None If 'english', a built-in stop word list for English is used. There are several known issues with 'english' and you should consider an alternative (see `Using stop words <https://scikit-learn.org/stable/modules/feature_extraction.html#using-stop-words>`_). If a list, that list is assumed to contain stop words, all of which will be removed from the resulting tokens. Only applies if ``analyzer == 'word'``. If None, no stop words will be used.	None
	random_state random_state: int, RandomState instance or None, default=None Used during randomized svd. Pass an int for reproducible results across multiple function calls.	None
	vocabulary vocabulary: Mapping or iterable, default=None In case of "tfidf" vectorizer, the vocabulary mapping passed to the vectorizer. Either a Mapping (e.g., a dict) where keys are terms and values are indices in the feature matrix, or an iterable over terms.	None

	max_iter max_iter: int, default=100 The maximum number of iterations of the boosting process, i.e. the maximum number of trees for binary classification. For multiclass classification, `n_classes` trees per iteration are built.	200
	early_stopping early_stopping: 'auto' or bool, default='auto' If 'auto', early stopping is enabled if the sample size is larger than 10000 or if `X_val` and `y_val` are passed to `fit`. If True, early stopping is enabled, otherwise early stopping is disabled. .. versionadded:: 0.23	True
	random_state random_state: int, RandomState instance or None, default=None Pseudo-random number generator to control the subsampling in the binning process, and the train/validation data split if early stopping is enabled. Pass an int for reproducible output across multiple function calls. See :term:`Glossary <random_state>`.	42
	loss loss: {'log_loss'}, default='log_loss' The loss function to use in the boosting process. For binary classification problems, 'log_loss' is also known as logistic loss, binomial deviance or binary crossentropy. Internally, the model fits one tree per boosting iteration and uses the logistic sigmoid function (expit) as inverse link function to compute the predicted positive class probability. For multiclass classification problems, 'log_loss' is also known as multinomial deviance or categorical crossentropy. Internally, the model fits one tree per boosting iteration and per class and uses the softmax function as inverse link function to compute the predicted probabilities of the classes.	'log_loss'
	learning_rate learning_rate: float, default=0.1 The learning rate, also known as shrinkage. This is used as a multiplicative factor for the leaves values. Use ``1`` for no shrinkage.	0.1
	max_leaf_nodes max_leaf_nodes: int or None, default=31 The maximum number of leaves for each tree. Must be strictly greater than 1. If None, there is no maximum limit.	31
	max_depth max_depth: int or None, default=None The maximum depth of each tree. The depth of a tree is the number of edges to go from the root to the deepest leaf. Depth isn't constrained by default.	None
	min_samples_leaf min_samples_leaf: int, default=20 The minimum number of samples per leaf. For small datasets with less than a few hundred samples, it is recommended to lower this value since only very shallow trees would be built.	20
	l2_regularization l2_regularization: float, default=0 The L2 regularization parameter penalizing leaves with small hessians. Use ``0`` for no regularization (default).	0.0
	max_features max_features: float, default=1.0 Proportion of randomly chosen features in each and every node split. This is a form of regularization, smaller values make the trees weaker learners and might prevent overfitting. If interaction constraints from `interaction_cst` are present, only allowed features are taken into account for the subsampling. .. versionadded:: 1.4	1.0
	max_bins max_bins: int, default=255 The maximum number of bins to use for non-missing values. Before training, each feature of the input array `X` is binned into integer-valued bins, which allows for a much faster training stage. Features with a small number of unique values may use less than ``max_bins`` bins. In addition to the ``max_bins`` bins, one more bin is always reserved for missing values. Must be no larger than 255.	255
	categorical_features categorical_features: array-like of {bool, int, str} of shape (n_features) or shape (n_categorical_features,), default='from_dtype' Indicates the categorical features. - None : no feature will be considered categorical. - boolean array-like : boolean mask indicating categorical features. - integer array-like : integer indices indicating categorical features. - str array-like: names of categorical features (assuming the training data has feature names). - `"from_dtype"`: dataframe columns with dtype "Categorical" and "Enum" are considered to be categorical features. The input must be a dataframe that is supported by narwhals (or supports it): :func:`narwhals.from_native` must work. This is the case, for instance, for pandas and polars DataFrames. For each categorical feature, there must be at most `max_bins` unique categories. Negative values for categorical features encoded as numeric dtypes are treated as missing values. All categorical values are converted to floating point numbers. This means that categorical values of 1.0 and 1 are treated as the same category. Read more in the :ref:`User Guide <categorical_support_gbdt>`. .. versionadded:: 0.24 .. versionchanged:: 1.2 Added support for feature names. .. versionchanged:: 1.4 Added `"from_dtype"` option. .. versionchanged:: 1.6 The default value changed from `None` to `"from_dtype"`.	'from_dtype'
	monotonic_cst monotonic_cst: array-like of int of shape (n_features) or dict, default=None Monotonic constraint to enforce on each feature are specified using the following integer values: - 1: monotonic increase - 0: no constraint - -1: monotonic decrease If a dict with str keys, map feature to monotonic constraints by name. If an array, the features are mapped to constraints by position. See :ref:`monotonic_cst_features_names` for a usage example. The constraints are only valid for binary classifications and hold over the probability of the positive class. Read more in the :ref:`User Guide <monotonic_cst_gbdt>`. .. versionadded:: 0.23 .. versionchanged:: 1.2 Accept dict of constraints with feature names as keys.	None
	interaction_cst interaction_cst: {"pairwise", "no_interactions"} or sequence of lists/tuples/sets of int, default=None Specify interaction constraints, the sets of features which can interact with each other in child node splits. Each item specifies the set of feature indices that are allowed to interact with each other. If there are more features than specified in these constraints, they are treated as if they were specified as an additional set. The strings "pairwise" and "no_interactions" are shorthands for allowing only pairwise or no interactions, respectively. For instance, with 5 features in total, `interaction_cst=[{0, 1}]` is equivalent to `interaction_cst=[{0, 1}, {2, 3, 4}]`, and specifies that each branch of a tree will either only split on features 0 and 1 or only split on features 2, 3 and 4. See :ref:`this example<ice-vs-pdp>` on how to use `interaction_cst`. .. versionadded:: 1.2	None
	warm_start warm_start: bool, default=False When set to ``True``, reuse the solution of the previous call to fit and add more estimators to the ensemble. For results to be valid, the estimator should be re-trained on the same data only. See :term:`the Glossary <warm_start>`.	False
	scoring scoring: str or callable or None, default='loss' Scoring method to use for early stopping. Only used if `early_stopping` is enabled. Options: - str: see :ref:`scoring_string_names` for options. - callable: a scorer callable object (e.g., function) with signature ``scorer(estimator, X, y)``. See :ref:`scoring_callable` for details. - `None`: :ref:`accuracy <accuracy_score>` is used. - 'loss': early stopping is checked w.r.t the loss value.	'loss'
	validation_fraction validation_fraction: int or float or None, default=0.1 Proportion (or absolute size) of training data to set aside as validation data for early stopping. If None, early stopping is done on the training data. The value is ignored if either early stopping is not performed, e.g. `early_stopping=False`, or if `X_val` and `y_val` are passed to fit.	0.1
	n_iter_no_change n_iter_no_change: int, default=10 Used to determine when to "early stop". The fitting process is stopped when none of the last ``n_iter_no_change`` scores are better than the ``n_iter_no_change - 1`` -th-to-last one, up to some tolerance. Only used if early stopping is performed.	10
	tol tol: float, default=1e-7 The absolute tolerance to use when comparing scores. The higher the tolerance, the more likely we are to early stop: higher tolerance means that it will be harder for subsequent iterations to be considered an improvement upon the reference score.	1e-07
	verbose verbose: int, default=0 The verbosity level. If not zero, print some information about the fitting process. ``1`` prints only summary info, ``2`` prints info per iteration.	0
	class_weight class_weight: dict or 'balanced', default=None Weights associated with classes in the form `{class_label: weight}`. If not given, all classes are supposed to have weight one. The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as `n_samples / (n_classes * np.bincount(y))`. Note that these weights will be multiplied with sample_weight (passed through the fit method) if `sample_weight` is specified. .. versionadded:: 1.2	None

gender

department

department_name

division

assignment_category

employee_position_title

date_first_hired

year_first_hired

gender

department

department_name

division

assignment_category

employee_position_title

date_first_hired

year_first_hired

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high_earner

high_earner

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