iris_data:用于分类的三类鸢尾花数据集

一个将 iris 数据集加载到 NumPy 数组中的函数。

# 鸢尾花数据集

本节将介绍如何使用`mlxtend`库中的鸢尾花数据集。

概述

用于分类的鸢尾花数据集。

特征

  1. 花萼长度
  2. 花萼宽度
  3. 花瓣长度
  4. 花瓣宽度

  5. 样本数量:150

  6. 目标变量(离散):{50个山鸢尾, 50个变色鸢尾, 50个维吉尼亚鸢尾}

参考文献

示例 1 - 数据集概述

from mlxtend.data import iris_data
X, y = iris_data()

print('Dimensions: %s x %s' % (X.shape[0], X.shape[1]))
print('\nHeader: %s' % ['sepal length', 'sepal width',
                        'petal length', 'petal width'])
print('1st row', X[0])

Dimensions: 150 x 4

Header: ['sepal length', 'sepal width', 'petal length', 'petal width']
1st row [5.1 3.5 1.4 0.2]
import numpy as np
print('Classes: Setosa, Versicolor, Virginica')
print(np.unique(y))
print('Class distribution: %s' % np.bincount(y))

Classes: Setosa, Versicolor, Virginica
[0 1 2]
Class distribution: [50 50 50]

API

iris_data(version='uci')

Iris flower dataset.

Parameters

Returns

Note

The Iris dataset (originally collected by Edgar Anderson) and available in UCI's machine learning repository is different from the Iris dataset described in the original paper by R.A. Fisher [1]). Precisely, there are two data points (row number 34 and 37) in UCI's Machine Learning repository are different from the origianlly published Iris dataset. Also, the original version of the Iris Dataset, which can be loaded via version='corrected' is the same as the one in R.

[1] . A. Fisher (1936). "The use of multiple measurements in taxonomic
problems". Annals of Eugenics. 7 (2): 179–188

Examples

For usage examples, please see https://rasbt.github.io/mlxtend/user_guide/data/iris_data/