aegarciaherrera/iris-clase

鸢尾花数据集是机器学习中非常经典的数据集，常用于分类和聚类任务。该数据集包含150个鸢尾花样本，每个样本都有四个特征：花萼长度、花萼宽度、花瓣长度和花瓣宽度。任务是根据这些特征将样本分类为三种物种之一：*Iris setosa*、*Iris versicolor*或*Iris virginica*。该数据集对于监督学习（分类）、无监督学习（聚类）、模型解释技术、特征选择和降维等都非常有用。

The Iris dataset is one of the most classic datasets in machine learning, often used for classification and clustering tasks. It contains 150 samples of iris flowers, each described by four features: sepal length, sepal width, petal length, and petal width. The task is to classify the samples into one of three species: *Iris setosa*, *Iris versicolor*, or *Iris virginica*. This dataset is especially useful for supervised learning (classification), unsupervised learning (clustering), model explainability techniques, feature selection, and dimensionality reduction.