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  • python3教程之初识人工智能(二):机器学习(三):sklearn数据集(4)

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  • 2 2]
  • .. _iris_dataset:
  •  
  • Iris plants dataset
  • --------------------
  •  
  • **Data Set Characteristics:**
  •  
  • :Number of Instances: 150 (50 in each of three classes)
  • :Number of Attributes: 4 numeric, predictive attributes and the class
  • :Attribute Information:
  • - sepal length in cm
  • - sepal width in cm
  • - petal length in cm
  • - petal width in cm
  • - class:
  • - Iris-Setosa
  • - Iris-Versicolour
  • - Iris-Virginica
  •  
  • :Summary Statistics:
  •  
  • ============== ==== ==== ======= ===== ====================
  • Min Max Mean SD Class Correlation
  • ============== ==== ==== ======= ===== ====================
  • sepal length: 4.3 7.9 5.84 0.83 0.7826
  • sepal width: 2.0 4.4 3.05 0.43 -0.4194
  • petal length: 1.0 6.9 3.76 1.76 0.9490 (high!)
  • petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)
  • ============== ==== ==== ======= ===== ====================
  •  
  • :Missing Attribute Values: None
  • :Class Distribution: 33.3% for each of 3 classes.
  • :Creator: R.A. Fisher
  • :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)
  • :Date: July, 1988
  •  
  • The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken
  • from Fisher's paper. Note that it's the same as in R, but not as in the UCI
  • Machine Learning Repository, which has two wrong data points.
  •  
  • This is perhaps the best known database to be found in the
  • pattern recognition literature. Fisher's paper is a classic in the field and
  • is referenced frequently to this day. (See Duda & Hart, for example.) The
  • data set contains 3 classes of 50 instances each, where each class refers to a
  • type of iris plant. One class is linearly separable from the other 2; the
  • latter are NOT linearly separable from each other.
  •  
  • .. topic:: References
  •  
  • - Fisher, R.A. "The use of multiple measurements in taxonomic problems"
  • Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to
  • Mathematical Statistics" (John Wiley, NY, 1950).
  • - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.
  • (Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.
  • - Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System
  • Structure and Classification Rule for Recognition in Partially Exposed
  • Environments". IEEE Transactions on Pattern Analysis and Machine
  • Intelligence, Vol. PAMI-2, No. 1, 67-71.
  • - Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". IEEE Transactions
  • on Information Theory, May 1972, 431-433.
  • - See also: 1988 MLC Proceedings, 54-64. Cheeseman et al"s AUTOCLASS II
  • conceptual clustering system finds 3 classes in the data.
  • - Many, many more ...

    • 1.4 数据集进行分割

    sklearn.model_selection.train_test_split(*arrays, **options)

    x：数据集的特征值 y： 数据集的标签值 test_size： 测试集的大小，一般为float random_state： 随机数种子,不同的种子会造成不同的随机采样结果。相同的种子采样结果相同。

    return：训练集特征值，测试集特征值，训练标签，测试标签(默认随机取)

    

      

    1. 
from sklearn.datasets import load_iris
      2. 

    2. 
from sklearn.model_selection import train_test_split
      3. 

    3. 
 
      4. 

    4. 
li = load_iris()
      5. 

    5. 
 
      6. 

    6. 
# 注意返回值, 训练集 train x_train, y_train 测试集 test x_test, y_test
      7. 

    7. 
x_train, x_test, y_train, y_test = train_test_split(li.data, li.target, test_size=0.25)
      8. 

    8. 
 
      9. 

    9. 
print(

    

    


    

    


    

    

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