sklearn.datasets.load_iris

sklearn.datasets.load_iris(return_X_y=False)

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_y : boolean, 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:

data : Bunch

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

K-means Clustering

Concatenating multiple feature extraction methods

The Iris Dataset

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

SVM Exercise

Test with permutations the significance of a classification score

Univariate Feature Selection

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

GMM covariances

Receiver Operating Characteristic (ROC) with cross validation

Confusion matrix

Nested versus non-nested cross-validation

Receiver Operating Characteristic (ROC)

Precision-Recall

Nearest Neighbors Classification

Nearest Centroid Classification

Compare Stochastic learning strategies for MLPClassifier

Decision boundary of label propagation versus SVM on the Iris dataset

SVM with custom kernel

Plot different SVM classifiers in the iris dataset

RBF SVM parameters

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

Understanding the decision tree structure