ML之DT&RFR&ExtraTR&GBR：基于四種算法(DT、RFR、ExtraTR、GBR)對Boston(波士頓房價)數據集(506,13+1)進行價格回歸預測并對比各自性能

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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)

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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 criteriaare "mse" for the mean squared error, which is equal to variancereduction as feature selection criterion and minimizes the L2 lossusing the mean of each terminal node, "friedman_mse", which uses meansquared error with Friedman's improvement score for potential splits,and "mae" for the mean absolute error, which minimizes the L1 lossusing 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 classifyingdecision trees on various sub-samples of the dataset and use averagingto improve the predictive accuracy and control over-fitting.The sub-sample size is always the same as the originalinput 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 ofrandomized decision trees (a.k.a. extra-trees) on various sub-samplesof the dataset and use averaging to improve the predictive accuracyand 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 thegiven loss function.Read more in the :ref:`User Guide <gradient_boosting>`.

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