`sklearn.datasets`.fetch_covtype#

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.

Classes	7
Samples total	581012
Dimensionality	54
Features	int

Read more in the User Guide.

Parameters:

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 (data.data, data.target) 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.

Returns:

datasetBunch

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.
DESCRstr: Description of the forest covertype dataset.
feature_nameslist: 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.

Examples

>>> from sklearn.datasets import fetch_covtype
>>> cov_type = fetch_covtype()
>>> cov_type.data.shape
(581012, 54)
>>> cov_type.target.shape
(581012,)
>>> # 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

Scalable learning with polynomial kernel approximation

Evaluation of outlier detection estimators

sklearn.datasets.fetch_covtype#

Examples using sklearn.datasets.fetch_covtype#

`sklearn.datasets`.fetch_covtype#

Examples using `sklearn.datasets.fetch_covtype`#