ML之LiR&SGDR：基于二种算法(LiR、SGDR)对Boston(波士顿房价)数据集(506,13+1)进行价格回归预测并对比各自性能
目录
输出结果
设计思路
核心代码
输出结果
Boston House Prices dataset===========================Notes------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.http://archive.ics.uci.edu/ml/datasets/HousingThis 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. **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.- many more! (see http://archive.ics.uci.edu/ml/datasets/Housing)
设计思路
核心代码
from sklearn.linear_model import LinearRegressionlr = LinearRegression()lr.fit(X_train, y_train)lr_y_predict = lr.predict(X_test)lr.score(X_test, y_test)r2_score(y_test, lr_y_predict))ss_y.inverse_transform(lr_y_predict)ss_y.inverse_transform(y_test)ss_y.inverse_transform(lr_y_predict)sgdr = SGDRegressor()sgdr.fit(X_train, y_train)sgdr_y_predict = sgdr.predict(X_test)sgdr.score(X_test, y_test)mean_squared_error(ss_y.inverse_transform(y_test)ss_y.inverse_transform(sgdr_y_predict)
ML之LiRSGDR：基于二种算法(LiR SGDR)对Boston(波士顿房价)数据集(506 13+1)进行价格回归预测并对比各自性能
