ML之回归预测：利用十类机器学习算法(线性回归、kNN、SVM、决策树、随机森林、极端随机树、SGD、提升树、LightGBM、XGBoost)对波士顿数据集【13+1,506】回归预测(模型评估、推理并导到csv)

目录

利用十类机器学习算法(线性回归、kNN、SVM、决策树、随机森林、极端随机树、SGD、提升树、LightGBM、XGBoost)对波士顿数据集【13+1,506】回归预测(模型评估、推理并导到csv)

输出数据集

1、LiR 线性回归算法

2、kNNR k最近邻算法

3、SVMR 支持向量机算法

4、DTR 决策树算法

5、RFR 随机森林算法

6、ExtraTR 极端随机树算法

7、SGDR 随机梯度上升算法

8、GBR 提升树算法

9、LightGBMR 算法

10、XGBR 算法

模型评估效果综合比较

模型推理预测综合比较

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ML之回归预测：利用十类机器学习算法(线性回归、kNN、SVM、决策树、随机森林、极端随机树、SGD、提升树、LightGBM、XGBoost)对波士顿数据集回归预测(模型评估、推理并导到csv)
ML之回归预测：利用十类机器学习算法(线性回归、kNN、SVM、决策树、随机森林、极端随机树、SGD、提升树、LightGBM、XGBoost)对波士顿数据集回归预测(模型评估、推理并导到csv)实现

利用十类机器学习算法(线性回归、kNN、SVM、决策树、随机森林、极端随机树、SGD、提升树、LightGBM、XGBoost)对波士顿数据集【13+1,506】回归预测(模型评估、推理并导到csv)

输出数据集

数据集的描述:  
 .. _boston_dataset:
 
Boston house prices dataset
---------------------------
 
**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.
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.
数据的初步查验：输出回归目标值target的差异
target_max 50.0
target_min 5.0
target_avg 22.532806324110677

1、LiR 线性回归算法

LiR Score value: 0.6757955014529482
LiR R2    value: 0.6757955014529482
LiR MAE   value: 3.5325325437053974
LiR MSE   value: 25.13923652035344

2、kNNR k最近邻算法

3、SVMR 支持向量机算法

4、DTR 决策树算法

5、RFR 随机森林算法

6、ExtraTR 极端随机树算法

7、SGDR 随机梯度上升算法

8、GBR 提升树算法

9、LightGBMR 算法

10、XGBR 算法

模型评估效果综合比较

模型推理预测综合比较