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
相关文章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
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