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A multivariate data set of 150 samples from three species of Iris flowers, used for linear discriminant analysis and machine learning. Learn about the features, classification, visualization and usage of the data set in R and Python.
Iris flower data set used for multi-class classification.
Sep 6, 2024 · Originally published at UCI Machine Learning Repository: Iris Data Set, this small dataset from 1936 is often used for testing out machine learning algorithms and visualizations (for example, Scatter Plot). Each row of the table represents an iris flower, including its species and dimensions of its botanical parts, sepal and petal, in centimeters.
Sepal_lengthSepal_widthPetal_lengthPetal_width5.13.51.40.24.93.01.40.24.73.21.30.24.63.11.50.2Learn how to load, plot and analyze the Iris dataset, a classic machine learning dataset with 150 samples of three iris types. See how to use PCA to visualize the first three principal components of the data.
A classic dataset of 150 iris plants with 4 features and 3 classes, widely used for evaluating classification methods. The dataset includes variable descriptions, baseline model performance, papers citing the dataset, and reviews.
May 15, 2024 · The Iris dataset consists of 150 samples of iris flowers from three different species: Setosa, Versicolor, and Virginica. Each sample includes four features: sepal length, sepal width, petal length, and petal width. It was introduced by the British biologist and statistician Ronald Fisher in 1936 as an example of discriminant analysis.
Explore the famous Iris flower data set with Python and visualization tools. Learn how to import, clean, describe, and plot the data for three species of Iris flowers.
