getml.dataset

The getml.datasets module includes utilities to load datasets, including methods to load and fetch popular reference datasets. It also features some artificial data generators.

Functions

load_air_pollution([roles, as_pandas])	Regression dataset on air pollution in Beijing, China
load_atherosclerosis([roles, as_pandas])	Binary classification dataset on the lethality of atherosclerosis
load_biodegradability([roles, as_pandas])	Regression dataset on molecule weight prediction
load_consumer_expenditures([roles, units, …])	Binary classification dataset on consumer expenditures
load_interstate94([roles, units, as_pandas])	Regression dataset on traffic volume predicition
load_loans([roles, units, as_pandas])	Binary classification dataset on loan default
load_occupancy([roles, as_pandas])	Binary classification dataset on occupancy detection
make_categorical([n_rows_population, …])	Generate a random dataset with categorical variables
make_discrete([n_rows_population, …])	Generate a random dataset with categorical variables HALLO HALLO The dataset consists of a population table and one peripheral table.
make_numerical([n_rows_population, …])	Generate a random dataset with continous numerical variables
make_same_units_categorical([…])	Generate a random dataset with categorical variables
make_same_units_numerical([…])	Generate a random dataset with continous numerical variables
make_snowflake([n_rows_population, …])	Generate a random dataset with continous numerical variables