from sklearn.datasets import load_iris
iris = load_iris()
print(iris.keys())
n_samples, n_features = iris.data.shape
print((n_samples, n_features))
print(iris.data[0])
print(iris.target.shape)
print(iris.target)
print(iris.target_names)
print("feature_names:",iris.feature_names)

sklearn中的iris数据集有5个key：

• [‘target_names’, ‘data’, ‘target’, ‘DESCR’, ‘feature_names’]
  
  • target_names : 分类名称
    
    • [‘setosa’ ‘versicolor’ ‘virginica’]
  • target：分类（150个）
    
    • (150L,)
    • [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]
  • feature_names: 特征名称
    
    • (‘feature_names:’, [‘sepal length (cm)’, ‘sepal width (cm)’, ‘petal length (cm)’, ‘petal width (cm)’])
  • data : 特征值
    
    • (150L, 4L)
    • data[0]:[ 5.1 3.5 1.4 0.2]