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# `sklearn.datasets`.fetch_species_distributions

sklearn.datasets.fetch_species_distributions(*, data_home=None, download_if_missing=True)

  Loader for species distribution dataset from Phillips et. al. (2006).

  Read more in the User Guide.

  Parameters:
    data_home str 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_missing bool, 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.

  Returns:
    data `Bunch`

      Dictionary-like object, with the following attributes.

      coveragesarray, shape = [14, 1592, 1212]

        These represent the 14 features measured at each point of the map grid. The latitude/longitude values for the grid are discussed below. Missing data is represented by the value -9999.

      trainrecord array, shape = (1624,)

        The training points for the data. Each point has three fields:

        - train[‘species’] is the species name

        - train[‘dd long’] is the longitude, in degrees

        - train[‘dd lat’] is the latitude, in degrees

      testrecord array, shape = (620,)

        The test points for the data. Same format as the training data.

      Nx, Nyintegers

        The number of longitudes (x) and latitudes (y) in the grid

      x_left_lower_corner, y_left_lower_cornerfloats

        The (x,y) position of the lower-left corner, in degrees

      grid_sizefloat

        The spacing between points of the grid, in degrees

  Notes

  This dataset represents the geographic distribution of species. The dataset is provided by Phillips et. al. (2006).

  The two species are:

  - “Bradypus variegatus” , the Brown-throated Sloth.

  - “Microryzomys minutus” , also known as the Forest Small Rice Rat, a rodent that lives in Peru, Colombia, Ecuador, Peru, and Venezuela.

  - For an example of using this dataset with scikit-learn, see examples/applications/plot_species_distribution_modeling.py.

  References

  - “Maximum entropy modeling of species geographic distributions” S. J. Phillips, R. P. Anderson, R. E. Schapire - Ecological Modelling, 190:231-259, 2006.

## Examples using `sklearn.datasets.fetch_species_distributions`

Species distribution modeling

Species distribution modeling

Kernel Density Estimate of Species Distributions

Kernel Density Estimate of Species Distributions