sklearn.datasets.load_iris¶

sklearn.datasets.load_iris(return_X_y=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.

Parameters

return_X_yboolean, default=False.

If True, returns (data, target) instead of a Bunch object. See below for more information about the data and target object.

New in version 0.18.

Returns

dataBunch

Dictionary-like object, the interesting attributes are: ‘data’, the data to learn, ‘target’, the classification labels, ‘target_names’, the meaning of the labels, ‘feature_names’, the meaning of the features, ‘DESCR’, the full description of the dataset, ‘filename’, the physical location of iris csv dataset (added in version 0.20).

(data, target)tuple if return_X_y is True

New in version 0.18.

Notes

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.

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)
['setosa', 'versicolor', 'virginica']

Examples using sklearn.datasets.load_iris¶

Plot classification probability¶

Plot Hierarchical Clustering Dendrogram¶

K-means Clustering¶

The Iris Dataset¶

Plot the decision surface of a decision tree on the iris dataset¶

Understanding the decision tree structure¶

PCA example with Iris Data-set¶

Incremental PCA¶

Comparison of LDA and PCA 2D projection of Iris dataset¶

Plot the decision boundaries of a VotingClassifier¶

Early stopping of Gradient Boosting¶

Plot the decision surfaces of ensembles of trees on the iris dataset¶

Test with permutations the significance of a classification score¶

Univariate Feature Selection¶

GMM covariances¶

Gaussian process classification (GPC) on iris dataset¶

Regularization path of L1- Logistic Regression¶

Logistic Regression 3-class Classifier¶

Plot multi-class SGD on the iris dataset¶

Confusion matrix¶

Receiver Operating Characteristic (ROC) with cross validation¶

Nested versus non-nested cross-validation¶

Receiver Operating Characteristic (ROC)¶

Precision-Recall¶

Nearest Neighbors Classification¶

Nearest Centroid Classification¶

Comparing Nearest Neighbors with and without Neighborhood Components Analysis¶

Compare Stochastic learning strategies for MLPClassifier¶

Concatenating multiple feature extraction methods¶

Release Highlights for scikit-learn 0.22¶

Decision boundary of label propagation versus SVM on the Iris dataset¶

SVM with custom kernel¶

SVM-Anova: SVM with univariate feature selection¶

Plot different SVM classifiers in the iris dataset¶

RBF SVM parameters¶

SVM Exercise¶