sklearn.datasets
.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.
- 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.New 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.New 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
.New in version 0.23.
- DESCR: str
The full description of the dataset.
- filename: str
The path to the location of the data.
New 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.
New in version 0.18.
- data
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']
See The Iris Dataset for a more detailed example of how to work with the iris dataset.
Examples using sklearn.datasets.load_iris
¶
Release Highlights for scikit-learn 1.2
Release Highlights for scikit-learn 0.24
Release Highlights for scikit-learn 0.22
Plot classification probability
Plot Hierarchical Clustering Dendrogram
Plot the decision surface of decision trees trained on the iris dataset
Understanding the decision tree structure
Comparison of LDA and PCA 2D projection of Iris dataset
Factor Analysis (with rotation) to visualize patterns
PCA example with Iris Data-set
Early stopping of Gradient Boosting
Plot the decision boundaries of a VotingClassifier
Plot the decision surfaces of ensembles of trees on the iris dataset
Gaussian process classification (GPC) on iris dataset
Logistic Regression 3-class Classifier
Plot multi-class SGD on the iris dataset
Regularization path of L1- Logistic Regression
Introducing the set_output API
Multiclass Receiver Operating Characteristic (ROC)
Nested versus non-nested cross-validation
Receiver Operating Characteristic (ROC) with cross validation
Test with permutations the significance of a classification score
Comparing Nearest Neighbors with and without Neighborhood Components Analysis
Nearest Centroid Classification
Nearest Neighbors Classification
Compare Stochastic learning strategies for MLPClassifier
Concatenating multiple feature extraction methods
Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset
Plot different SVM classifiers in the iris dataset
SVM-Anova: SVM with univariate feature selection