sklearn.datasets.fetch_covtype(*, data_home=None, download_if_missing=True, random_state=None, shuffle=False, return_X_y=False, as_frame=False)[source]

Load the covertype dataset (classification).

Download it if necessary.



Samples total






Read more in the User Guide.

data_homestr or path-like, default=None

Specify another download and cache folder for the datasets. By default all scikit-learn data is stored in ‘~/scikit_learn_data’ subfolders.

download_if_missingbool, default=True

If False, raise an OSError if the data is not locally available instead of trying to download the data from the source site.

random_stateint, RandomState instance or None, default=None

Determines random number generation for dataset shuffling. Pass an int for reproducible output across multiple function calls. See Glossary.

shufflebool, default=False

Whether to shuffle dataset.

return_X_ybool, default=False

If True, returns (, instead of a Bunch object.

New in version 0.20.

as_framebool, default=False

If True, the data is a pandas DataFrame including columns with appropriate dtypes (numeric). The target is a pandas DataFrame or Series depending on the number of target columns. If return_X_y is True, then (data, target) will be pandas DataFrames or Series as described below.

New in version 0.24.


Dictionary-like object, with the following attributes.

datandarray of shape (581012, 54)

Each row corresponds to the 54 features in the dataset.

targetndarray of shape (581012,)

Each value corresponds to one of the 7 forest covertypes with values ranging between 1 to 7.

framedataframe of shape (581012, 55)

Only present when as_frame=True. Contains data and target.


Description of the forest covertype dataset.


The names of the dataset columns.

target_names: list

The names of the target columns.

(data, target)tuple if return_X_y is True

A tuple of two ndarray. The first containing a 2D array of shape (n_samples, n_features) with each row representing one sample and each column representing the features. The second ndarray of shape (n_samples,) containing the target samples.

New in version 0.20.


>>> from sklearn.datasets import fetch_covtype
>>> cov_type = fetch_covtype()
(581012, 54)
>>> # Let's check the 4 first feature names
>>> cov_type.feature_names[:4]
['Elevation', 'Aspect', 'Slope', 'Horizontal_Distance_To_Hydrology']

Examples using sklearn.datasets.fetch_covtype

Release Highlights for scikit-learn 0.24

Release Highlights for scikit-learn 0.24

Scalable learning with polynomial kernel approximation

Scalable learning with polynomial kernel approximation

Evaluation of outlier detection estimators

Evaluation of outlier detection estimators