load_data#

load_data(data_format: str = 'Dataset', as_train_test: bool = True) Union[Tuple, Dataset, DataFrame][source]#

Load and returns the Adult dataset (classification).

The data has 48842 records with 14 features and one binary target column, referring to whether the person’s income is greater than 50K.

This is a copy of UCI ML Adult dataset. https://archive.ics.uci.edu/ml/datasets/adult

References:
  • Ron Kohavi, “Scaling Up the Accuracy of Naive-Bayes Classifiers: a Decision-Tree Hybrid”, Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, 1996

The typical ML task in this dataset is to build a model that determines whether a person makes over 50K a year.

Dataset Shape:
Dataset Shape#

Property

Value

Samples Total

48842

Dimensionality

14

Features

real, string

Targets

2

Samples per class

‘>50K’ - 23.93%, ‘<=50K’ - 76.07%

Description:
Dataset Description#

Column name

Column Role

Description

Age

Feature

The age of the person.

workclass

Feature

[Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked]

fnlwgt

Feature

Final weight.

education

Feature

[Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters,

1st-4th, 10th, Doctorate, 5th-6th, Preschool]

education-num

Feature

Number of years of education

marital-status

Feature

[Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent,

Married-AF-spouse]

occupation

Feature

[Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners,

Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces]

relationship

Feature

[Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried]

race

Feature

[White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black]

sex

Feature

[Male, Female]

capital-gain

Feature

The capital gain of the person

capital-loss

Feature

The capital loss of the person

hours-per-week

Feature

The number of hours worked per week

native-country

Feature

[United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India,

Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands]

target

Target

The target variable, whether the person makes over 50K a year.

Parameters
data_formatstr, default: ‘Dataset’

Represent the format of the returned value. Can be ‘Dataset’|’Dataframe’ ‘Dataset’ will return the data as a Dataset object ‘Dataframe’ will return the data as a pandas Dataframe object

as_train_testbool, default: True

If True, the returned data is splitted into train and test exactly like the toy model was trained. The first return value is the train data and the second is the test data. In order to get this model, call the load_fitted_model() function. Otherwise, returns a single object.

Returns
datasetUnion[deepchecks.Dataset, pd.DataFrame]

the data object, corresponding to the data_format attribute.

train, testTuple[Union[deepchecks.Dataset, pd.DataFrame],Union[deepchecks.Dataset, pd.DataFrame]

tuple if as_train_test = True. Tuple of two objects represents the dataset splitted to train and test sets.