ML之回歸預測：利用十類機器學習算法(線性回歸、kNN、SVM、決策樹、隨機森林、極端隨機樹、SGD、提升樹、LightGBM、XGBoost)對波士頓數據集【13+1,506】回歸預測(模型評估、推理并導到csv)

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目錄

利用十類機器學習算法(線性回歸、kNN、SVM、決策樹、隨機森林、極端隨機樹、SGD、提升樹、LightGBM、XGBoost)對波士頓數據集【13+1,506】回歸預測(模型評估、推理并導到csv)

輸出數據集

1、LiR 線性回歸算法

2、kNNR k最近鄰算法

3、SVMR 支持向量機算法

4、DTR 決策樹算法

5、RFR 隨機森林算法

6、ExtraTR 極端隨機樹算法

7、SGDR 隨機梯度上升算法

8、GBR 提升樹算法

9、LightGBMR 算法

10、XGBR 算法

模型評估效果綜合比較

模型推理預測綜合比較

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相關文章
ML之回歸預測：利用十類機器學習算法(線性回歸、kNN、SVM、決策樹、隨機森林、極端隨機樹、SGD、提升樹、LightGBM、XGBoost)對波士頓數據集回歸預測(模型評估、推理并導到csv)
ML之回歸預測：利用十類機器學習算法(線性回歸、kNN、SVM、決策樹、隨機森林、極端隨機樹、SGD、提升樹、LightGBM、XGBoost)對波士頓數據集回歸預測(模型評估、推理并導到csv)實現

利用十類機器學習算法(線性回歸、kNN、SVM、決策樹、隨機森林、極端隨機樹、SGD、提升樹、LightGBM、XGBoost)對波士頓數據集【13+1,506】回歸預測(模型評估、推理并導到csv)

輸出數據集

數據集的描述: .. _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.數據的初步查驗：輸出回歸目標值target的差異 target_max 50.0 target_min 5.0 target_avg 22.532806324110677

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1、LiR 線性回歸算法

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

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2、kNNR k最近鄰算法

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3、SVMR 支持向量機算法

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4、DTR 決策樹算法

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5、RFR 隨機森林算法

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6、ExtraTR 極端隨機樹算法

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7、SGDR 隨機梯度上升算法

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8、GBR 提升樹算法

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9、LightGBMR 算法

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10、XGBR 算法

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模型評估效果綜合比較

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模型推理預測綜合比較

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總結

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