ML之DT&RFR&ExtraTR&GBR：基于四种算法(DT、RFR、ExtraTR、GBR)对波士顿房价数据集(506,13+1)进行价格回归预测并对比各自性能

ML之DT&RFR&ExtraTR&GBR：基于四种算法(DT、RFR、ExtraTR、GBR)对波士顿房价数据集(506,13+1)进行价格回归预测并对比各自性能

 

输出结果

Boston House Prices dataset
===========================
 
Notes
------
Data Set Characteristics:  
 
    :Number of Instances: 506 
 
    :Number of Attributes: 13 numeric/categorical predictive
    
    :Median Value (attribute 14) is usually the target
 
    :Attribute Information (in order):
        - CRIM     per capita crime rate by town
        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.
        - INDUS    proportion of non-retail business acres per town
        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)
        - NOX      nitric oxides concentration (parts per 10 million)
        - RM       average number of rooms per dwelling
        - 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.
http://archive.ics.uci.edu/ml/datasets/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.   
     
**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.
   - many more! (see http://archive.ics.uci.edu/ml/datasets/Housing)

 

 

 

设计思路

 

核心代码

class DecisionTreeRegressor(BaseDecisionTree, RegressorMixin):
    """A decision tree regressor.
    
    Read more in the :ref:`User Guide <tree>`.
    
    Parameters
    ----------
    criterion : string, optional (default="mse")
    The function to measure the quality of a split. Supported criteria
    are "mse" for the mean squared error, which is equal to variance
    reduction as feature selection criterion and minimizes the L2 
     loss
    using the mean of each terminal node, "friedman_mse", which 
     uses mean
    squared error with Friedman's improvement score for potential 
     splits,
    and "mae" for the mean absolute error, which minimizes the L1 
     loss
    using the median of each terminal node.


class RandomForestRegressor(ForestRegressor):
    """A random forest regressor.
    
    A random forest is a meta estimator that fits a number of classifying
    decision trees on various sub-samples of the dataset and use averaging
    to improve the predictive accuracy and control over-fitting.
    The sub-sample size is always the same as the original
    input sample size but the samples are drawn with replacement if
    `bootstrap=True` (default).
    
    Read more in the :ref:`User Guide <forest>`.


class ExtraTreesRegressor(ForestRegressor):
    """An extra-trees regressor.
    
    This class implements a meta estimator that fits a number of
    randomized decision trees (a.k.a. extra-trees) on various sub-samples
    of the dataset and use averaging to improve the predictive accuracy
    and control over-fitting.
    
    Read more in the :ref:`User Guide <forest>`.




class GradientBoostingRegressor(BaseGradientBoosting, RegressorMixin):
    """Gradient Boosting for regression.
    
    GB builds an additive model in a forward stage-wise fashion;
    it allows for the optimization of arbitrary differentiable loss functions.
    In each stage a regression tree is fit on the negative gradient of the
    given loss function.
    
    Read more in the :ref:`User Guide <gradient_boosting>`.