load_iris#
- sklearn.datasets.load_iris(*, return_X_y=False, as_frame=False)[source]#
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 thedata
andtarget
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:
- data
Bunch
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 withdata
andtarget
.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.
- data
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 Principal Component Analysis (PCA) on Iris Dataset for a more detailed example of how to work with the iris dataset.
Gallery examples#
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Plot the decision surface of decision trees trained on the iris dataset
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Comparison of LDA and PCA 2D projection of Iris dataset
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Factor Analysis (with rotation) to visualize patterns
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Principal Component Analysis (PCA) on Iris Dataset
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Plot the decision boundaries of a VotingClassifier
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Plot the decision surfaces of ensembles of trees on the iris dataset
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Gaussian process classification (GPC) on iris dataset
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Multiclass Receiver Operating Characteristic (ROC)
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Receiver Operating Characteristic (ROC) with cross validation
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Test with permutations the significance of a classification score
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Comparing Nearest Neighbors with and without Neighborhood Components Analysis
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Compare Stochastic learning strategies for MLPClassifier
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Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset
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Plot different SVM classifiers in the iris dataset