sklearn.datasets.load_iris

sklearn.datasets.load_iris()

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

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, and ‘DESCR’, the full description of the dataset.

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

Concatenating multiple feature extraction methods

Plot classification probability

K-means Clustering

The Iris Dataset

Comparison of LDA and PCA 2D projection of Iris dataset

Incremental PCA

PCA example with Iris Data-set

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

Logistic Regression 3-class Classifier

Path with L1- Logistic Regression

Plot multi-class SGD on the iris dataset

GMM classification

Confusion matrix

Receiver Operating Characteristic (ROC) with cross validation

Receiver Operating Characteristic (ROC)

Precision-Recall

Nearest Neighbors Classification

Nearest Centroid Classification

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