200字范文 > 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

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

时间：2019-07-13 14:18:00

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)

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):- CRIMper 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- CHASCharles River dummy variable (= 1 if tract bounds river; 0 otherwise)- NOXnitric oxides concentration (parts per 10 million)- RM average number of rooms per dwelling- AGEproportion of owner-occupied units built prior to 1940- DISweighted distances to five Boston employment centres- RADindex of accessibility to radial highways- TAXfull-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- MEDVMedian 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. 'Hedonicprices 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 onpages 244-261 of the latter.The Boston house-price data has been used in many machine learning papers that address regressionproblems. .. 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 50.0target_min 5.0target_avg 22.532806324110677