sklearn.datasets.fetch_openml  
sklearn.datasets.fetch_openml(name: Optional[str] = None, *, version: Union[str, int] = 'active', data_id: Optional[int] = None, data_home: Optional[str] = None, target_column: Optional[Union[str, List]] = 'default-target', cache: bool = True, return_X_y: bool = False, as_frame: Union[str, bool] = 'auto') [source]
 
Fetch dataset from openml by name or dataset id. Datasets are uniquely identified by either an integer ID or by a combination of name and version (i.e. there might be multiple versions of the ‘iris’ dataset). Please give either name or data_id (not both). In case a name is given, a version can also be provided. Read more in the User Guide.  New in version 0.20.   Note EXPERIMENTAL The API is experimental (particularly the return value structure), and might have small backward-incompatible changes without notice or warning in future releases.   Parameters 
 
namestr, default=None 

String identifier of the dataset. Note that OpenML can have multiple datasets with the same name.  
versionint or ‘active’, default=’active’ 

Version of the dataset. Can only be provided if also name is given. If ‘active’ the oldest version that’s still active is used. Since there may be more than one active version of a dataset, and those versions may fundamentally be different from one another, setting an exact version is highly recommended.  
data_idint, default=None 

OpenML ID of the dataset. The most specific way of retrieving a dataset. If data_id is not given, name (and potential version) are used to obtain a dataset.  
data_homestr, default=None 

Specify another download and cache folder for the data sets. By default all scikit-learn data is stored in ‘~/scikit_learn_data’ subfolders.  
target_columnstr, list or None, default=’default-target’ 

Specify the column name in the data to use as target. If ‘default-target’, the standard target column a stored on the server is used. If None, all columns are returned as data and the target is None. If list (of strings), all columns with these names are returned as multi-target (Note: not all scikit-learn classifiers can handle all types of multi-output combinations)  
cachebool, default=True 

Whether to cache downloaded datasets using joblib.  
return_X_ybool, default=False 

If True, returns (data, target) instead of a Bunch object. See below for more information about the data and target objects.  
as_framebool or ‘auto’, default=’auto’ 

If True, the data is a pandas DataFrame including columns with appropriate dtypes (numeric, string or categorical). The target is a pandas DataFrame or Series depending on the number of target_columns. The Bunch will contain a frame attribute with the target and the data. If return_X_y is True, then (data, target) will be pandas DataFrames or Series as describe above. If as_frame is ‘auto’, the data and target will be converted to DataFrame or Series as if as_frame is set to True, unless the dataset is stored in sparse format.  Changed in version 0.24: The default value of as_frame changed from False to 'auto' in 0.24.     Returns 
 
dataBunch 

Dictionary-like object, with the following attributes.  
datanp.array, scipy.sparse.csr_matrix of floats, or pandas DataFrame 

The feature matrix. Categorical features are encoded as ordinals.  
targetnp.array, pandas Series or DataFrame 

The regression target or classification labels, if applicable. Dtype is float if numeric, and object if categorical. If as_frame is True, target is a pandas object.  
DESCRstr 

The full description of the dataset  
feature_nameslist 

The names of the dataset columns  target_names: list

The names of the target columns    New in version 0.22.   
categoriesdict or None 

Maps each categorical feature name to a list of values, such that the value encoded as i is ith in the list. If as_frame is True, this is None.  
detailsdict 

More metadata from OpenML  
framepandas DataFrame 

Only present when as_frame=True. DataFrame with data and target.    
(data, target)tuple if return_X_y is True 

 Note EXPERIMENTAL This interface is experimental and subsequent releases may change attributes without notice (although there should only be minor changes to data and target).  Missing values in the ‘data’ are represented as NaN’s. Missing values in ‘target’ are represented as NaN’s (numerical target) or None (categorical target)     
 Examples using sklearn.datasets.fetch_openml
 
  Release Highlights for scikit-learn 0.22  

  Categorical Feature Support in Gradient Boosting  

  Combine predictors using stacking  

  Gaussian process regression (GPR) on Mauna Loa CO2 data.  

  MNIST classification using multinomial logistic + L1  

  Early stopping of Stochastic Gradient Descent  

  Poisson regression and non-normal loss  

  Tweedie regression on insurance claims  

  Permutation Importance vs Random Forest Feature Importance (MDI)  

  Common pitfalls in interpretation of coefficients of linear models  

  Visualizations with Display Objects  

  Classifier Chain  

  Approximate nearest neighbors in TSNE  

  Visualization of MLP weights on MNIST  

  Column Transformer with Mixed Types  

  Effect of transforming the targets in regression model