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  • 初识人工智能(二):机器学习(三):sklearn数据集(10)

- AGE proportion of owner-occupied units built prior to 1940
  • - DIS weighted distances to five Boston employment centres
  • - RAD index of accessibility to radial highways
  • - TAX full-value property-tax rate per $10,000
  • - PTRATIO pupil-teacher ratio by town
  • - B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town
  • - LSTAT % lower status of the population
  • - MEDV Median value of owner-occupied homes in $1000's
  •  
  • :Missing Attribute Values: None
  •  
  • :Creator: Harrison, D. and Rubinfeld, D.L.
  •  
  • This is a copy of UCI ML housing dataset.
  • https://archive.ics.uci.edu/ml/machine-learning-databases/housing/
  •  
  •  
  • This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.
  •  
  • The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic
  • prices and the demand for clean air', J. Environ. Economics & Management,
  • vol.5, 81-102, 1978. Used in Belsley, Kuh & Welsch, 'Regression diagnostics
  • ...', Wiley, 1980. N.B. Various transformations are used in the table on
  • pages 244-261 of the latter.
  •  
  • The Boston house-price data has been used in many machine learning papers that address regression
  • problems.
  •  
  • .. topic:: References
  •  
  • - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.
  • - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.

    • 1.7 转换器

    在之前我们做的特征工程有几个步骤？

    1、实例化 (实例化的是一个转换器类(Transformer)) 。 2、调用fit_transform()对于文档建立分类词频矩阵，不能同时调用)。

    fit_transform()：输入数据直接转换。

    其实fit_transform()方法就是fit()方法和transform()方法的结合。

    fit()：输入数据，但不做事情。

    transform()：进行数据的转换。

    
	
      
		
    1. 
			from sklearn.preprocessing import StandardScaler
      2. 
		
    2. 
			 
      3. 
		
    3. 
			s = StandardScaler()
      4. 
		
    4. 
			print(s.fit_transform([[1,2,3],[4,5,6]]))
      5. 
		
    5. 
			 
      6. 
		
    6. 
			ss = StandardScaler()
      7. 
		
    7. 
			print(ss.fit([[1,2,3],[4,5,6]]))
      8. 
		
    8. 
			print(ss.transform([[1,2,3],[4,5,6]]))
      9. 
		
    9. 
			print(ss.fit([[2,3,4],[4,5,7]]))
      10. 
		
    10. 
			print(ss.transform([[1,2,3],[4,5,6]]))
      11. 
	
    

    

    
	运行结果：
    

    
	
    

    
	
    

    
	1.8 估计器
    

    
	在sklearn中，估计器(estimator)是一个重要的角色，分类器和回归器都属于estimator，是一类实现了算法的API
    

    
	1、用于分类的估计器： sklearn.neighbors k-近邻算法 sklearn.naive_bayes 贝叶斯 sklearn.linear_model.LogisticRegression 逻辑回归
    

    
	2、用于回归的估计器： sklearn.linear_model.LinearRegression 线性回归 sklearn.linear_model.Ridge 岭回归
    

    
	估计器工作流程：
    

    
	
    

      



  
    
  
  
    
   
  
    
  
 
 
    
 
 
  
 
 
 
 

    




 



    


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