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)
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
1. 数据集的描述: 2. .. _boston_dataset: 3. 4. Boston house prices dataset 5. --------------------------- 6. 7. **Data Set Characteristics:** 8. 9. :Number of Instances: 506 10. 11. :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target. 12. 13. :Attribute Information (in order): 14. - CRIM per capita crime rate by town 15. - ZN proportion of residential land zoned for lots over 25,000 sq.ft. 16. - INDUS proportion of non-retail business acres per town 17. - CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise) 18. - NOX nitric oxides concentration (parts per 10 million) 19. - RM average number of rooms per dwelling 20. - AGE proportion of owner-occupied units built prior to 1940 21. - DIS weighted distances to five Boston employment centres 22. - RAD index of accessibility to radial highways 23. - TAX full-value property-tax rate per $10,000 24. - PTRATIO pupil-teacher ratio by town 25. - B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town 26. - LSTAT % lower status of the population 27. - MEDV Median value of owner-occupied homes in $1000's 28. 29. :Missing Attribute Values: None 30. 31. :Creator: Harrison, D. and Rubinfeld, D.L. 32. 33. This is a copy of UCI ML housing dataset. 34. https://archive.ics.uci.edu/ml/machine-learning-databases/housing/ 35. 36. 37. This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University. 38. 39. The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic 40. prices and the demand for clean air', J. Environ. Economics & Management, 41. vol.5, 81-102, 1978. Used in Belsley, Kuh & Welsch, 'Regression diagnostics 42. ...', Wiley, 1980. N.B. Various transformations are used in the table on 43. pages 244-261 of the latter. 44. 45. The Boston house-price data has been used in many machine learning papers that address regression 46. problems. 47. 48. .. topic:: References 49. 50. - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261. 51. - 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. 52. 53. 数据的初步查验：输出回归目标值target的差异 54. target_max 50.0 55. target_min 5.0 56. target_avg 22.532806324110677
1、LiR 线性回归算法
1. LiR Score value: 0.6757955014529482 2. LiR R2 value: 0.6757955014529482 3. LiR MAE value: 3.5325325437053974 4. 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
