load_iris#

sklearn.datasets.load_iris(*, return_X_y=False, as_frame=False)[المصدر]#

Load and return the iris dataset (classification).

The iris dataset is a classic and very easy multi-class classification dataset.

Classes

3

Samples per class

50

Samples total

150

Dimensionality

4

Features

real, positive

Read more in the User Guide.

Changed in version 0.20: Fixed two wrong data points according to Fisher's paper. The new version is the same as in R, but not as in the UCI Machine Learning Repository.

Parameters:
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 object.

Added in version 0.18.

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.

Added in version 0.23.

Returns:
dataBunch

Dictionary-like object, with the following attributes.

data{ndarray, dataframe} of shape (150, 4)

The data matrix. If as_frame=True, data will be a pandas DataFrame.

target: {ndarray, Series} of shape (150,)

The classification target. If as_frame=True, target will be a pandas Series.

feature_names: list

The names of the dataset columns.

target_names: list

The names of target classes.

frame: DataFrame of shape (150, 5)

Only present when as_frame=True. DataFrame with data and target.

Added in version 0.23.

DESCR: str

The full description of the dataset.

filename: str

The path to the location of the data.

Added in version 0.20.

(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.

Added in version 0.18.

Examples

Let's say you are interested in the samples 10, 25, and 50, and want to know their class name.

>>> from sklearn.datasets import load_iris
>>> data = load_iris()
>>> data.target[[10, 25, 50]]
array([0, 0, 1])
>>> list(data.target_names)
[np.str_('setosa'), np.str_('versicolor'), np.str_('virginica')]

See تحليل المكونات الرئيسية (PCA) على مجموعة بيانات Iris for a more detailed example of how to work with the iris dataset.