在 sklearn.datasets
有幾個數據庫可以提供給大家玩玩。
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
這是很有名的數據庫, 很多人都拿它當例子。
from sklearn.datasets import load_iris
iris = load_iris()
iris.data
array([[ 5.1, 3.5, 1.4, 0.2], [ 4.9, 3. , 1.4, 0.2], [ 4.7, 3.2, 1.3, 0.2], [ 4.6, 3.1, 1.5, 0.2], [ 5. , 3.6, 1.4, 0.2], [ 5.4, 3.9, 1.7, 0.4], [ 4.6, 3.4, 1.4, 0.3], [ 5. , 3.4, 1.5, 0.2], [ 4.4, 2.9, 1.4, 0.2], [ 4.9, 3.1, 1.5, 0.1], [ 5.4, 3.7, 1.5, 0.2], [ 4.8, 3.4, 1.6, 0.2], [ 4.8, 3. , 1.4, 0.1], [ 4.3, 3. , 1.1, 0.1], [ 5.8, 4. , 1.2, 0.2], [ 5.7, 4.4, 1.5, 0.4], [ 5.4, 3.9, 1.3, 0.4], [ 5.1, 3.5, 1.4, 0.3], [ 5.7, 3.8, 1.7, 0.3], [ 5.1, 3.8, 1.5, 0.3], [ 5.4, 3.4, 1.7, 0.2], [ 5.1, 3.7, 1.5, 0.4], [ 4.6, 3.6, 1. , 0.2], [ 5.1, 3.3, 1.7, 0.5], [ 4.8, 3.4, 1.9, 0.2], [ 5. , 3. , 1.6, 0.2], [ 5. , 3.4, 1.6, 0.4], [ 5.2, 3.5, 1.5, 0.2], [ 5.2, 3.4, 1.4, 0.2], [ 4.7, 3.2, 1.6, 0.2], [ 4.8, 3.1, 1.6, 0.2], [ 5.4, 3.4, 1.5, 0.4], [ 5.2, 4.1, 1.5, 0.1], [ 5.5, 4.2, 1.4, 0.2], [ 4.9, 3.1, 1.5, 0.1], [ 5. , 3.2, 1.2, 0.2], [ 5.5, 3.5, 1.3, 0.2], [ 4.9, 3.1, 1.5, 0.1], [ 4.4, 3. , 1.3, 0.2], [ 5.1, 3.4, 1.5, 0.2], [ 5. , 3.5, 1.3, 0.3], [ 4.5, 2.3, 1.3, 0.3], [ 4.4, 3.2, 1.3, 0.2], [ 5. , 3.5, 1.6, 0.6], [ 5.1, 3.8, 1.9, 0.4], [ 4.8, 3. , 1.4, 0.3], [ 5.1, 3.8, 1.6, 0.2], [ 4.6, 3.2, 1.4, 0.2], [ 5.3, 3.7, 1.5, 0.2], [ 5. , 3.3, 1.4, 0.2], [ 7. , 3.2, 4.7, 1.4], [ 6.4, 3.2, 4.5, 1.5], [ 6.9, 3.1, 4.9, 1.5], [ 5.5, 2.3, 4. , 1.3], [ 6.5, 2.8, 4.6, 1.5], [ 5.7, 2.8, 4.5, 1.3], [ 6.3, 3.3, 4.7, 1.6], [ 4.9, 2.4, 3.3, 1. ], [ 6.6, 2.9, 4.6, 1.3], [ 5.2, 2.7, 3.9, 1.4], [ 5. , 2. , 3.5, 1. ], [ 5.9, 3. , 4.2, 1.5], [ 6. , 2.2, 4. , 1. ], [ 6.1, 2.9, 4.7, 1.4], [ 5.6, 2.9, 3.6, 1.3], [ 6.7, 3.1, 4.4, 1.4], [ 5.6, 3. , 4.5, 1.5], [ 5.8, 2.7, 4.1, 1. ], [ 6.2, 2.2, 4.5, 1.5], [ 5.6, 2.5, 3.9, 1.1], [ 5.9, 3.2, 4.8, 1.8], [ 6.1, 2.8, 4. , 1.3], [ 6.3, 2.5, 4.9, 1.5], [ 6.1, 2.8, 4.7, 1.2], [ 6.4, 2.9, 4.3, 1.3], [ 6.6, 3. , 4.4, 1.4], [ 6.8, 2.8, 4.8, 1.4], [ 6.7, 3. , 5. , 1.7], [ 6. , 2.9, 4.5, 1.5], [ 5.7, 2.6, 3.5, 1. ], [ 5.5, 2.4, 3.8, 1.1], [ 5.5, 2.4, 3.7, 1. ], [ 5.8, 2.7, 3.9, 1.2], [ 6. , 2.7, 5.1, 1.6], [ 5.4, 3. , 4.5, 1.5], [ 6. , 3.4, 4.5, 1.6], [ 6.7, 3.1, 4.7, 1.5], [ 6.3, 2.3, 4.4, 1.3], [ 5.6, 3. , 4.1, 1.3], [ 5.5, 2.5, 4. , 1.3], [ 5.5, 2.6, 4.4, 1.2], [ 6.1, 3. , 4.6, 1.4], [ 5.8, 2.6, 4. , 1.2], [ 5. , 2.3, 3.3, 1. ], [ 5.6, 2.7, 4.2, 1.3], [ 5.7, 3. , 4.2, 1.2], [ 5.7, 2.9, 4.2, 1.3], [ 6.2, 2.9, 4.3, 1.3], [ 5.1, 2.5, 3. , 1.1], [ 5.7, 2.8, 4.1, 1.3], [ 6.3, 3.3, 6. , 2.5], [ 5.8, 2.7, 5.1, 1.9], [ 7.1, 3. , 5.9, 2.1], [ 6.3, 2.9, 5.6, 1.8], [ 6.5, 3. , 5.8, 2.2], [ 7.6, 3. , 6.6, 2.1], [ 4.9, 2.5, 4.5, 1.7], [ 7.3, 2.9, 6.3, 1.8], [ 6.7, 2.5, 5.8, 1.8], [ 7.2, 3.6, 6.1, 2.5], [ 6.5, 3.2, 5.1, 2. ], [ 6.4, 2.7, 5.3, 1.9], [ 6.8, 3. , 5.5, 2.1], [ 5.7, 2.5, 5. , 2. ], [ 5.8, 2.8, 5.1, 2.4], [ 6.4, 3.2, 5.3, 2.3], [ 6.5, 3. , 5.5, 1.8], [ 7.7, 3.8, 6.7, 2.2], [ 7.7, 2.6, 6.9, 2.3], [ 6. , 2.2, 5. , 1.5], [ 6.9, 3.2, 5.7, 2.3], [ 5.6, 2.8, 4.9, 2. ], [ 7.7, 2.8, 6.7, 2. ], [ 6.3, 2.7, 4.9, 1.8], [ 6.7, 3.3, 5.7, 2.1], [ 7.2, 3.2, 6. , 1.8], [ 6.2, 2.8, 4.8, 1.8], [ 6.1, 3. , 4.9, 1.8], [ 6.4, 2.8, 5.6, 2.1], [ 7.2, 3. , 5.8, 1.6], [ 7.4, 2.8, 6.1, 1.9], [ 7.9, 3.8, 6.4, 2. ], [ 6.4, 2.8, 5.6, 2.2], [ 6.3, 2.8, 5.1, 1.5], [ 6.1, 2.6, 5.6, 1.4], [ 7.7, 3. , 6.1, 2.3], [ 6.3, 3.4, 5.6, 2.4], [ 6.4, 3.1, 5.5, 1.8], [ 6. , 3. , 4.8, 1.8], [ 6.9, 3.1, 5.4, 2.1], [ 6.7, 3.1, 5.6, 2.4], [ 6.9, 3.1, 5.1, 2.3], [ 5.8, 2.7, 5.1, 1.9], [ 6.8, 3.2, 5.9, 2.3], [ 6.7, 3.3, 5.7, 2.5], [ 6.7, 3. , 5.2, 2.3], [ 6.3, 2.5, 5. , 1.9], [ 6.5, 3. , 5.2, 2. ], [ 6.2, 3.4, 5.4, 2.3], [ 5.9, 3. , 5.1, 1.8]])
輸出就是某筆數據是哪種 (事實上是哪個亞屬) 的鳶尾花, 共分為 0, 1, 2 三種。
iris.target
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])
我們只選用花萼長度、花萼寬度 (當然只是例子, 事實上四個參數很少, 全部一起來也可以) 當輸入資料。
x = iris.data[:, :2]
y = iris.target
只選兩個 features 原因之一是好畫 :)
plt.scatter(x[:,0], x[:,1], s=50, c=y)
<matplotlib.collections.PathCollection at 0x107d5cfd0>