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.