ML之LiR&SGDR：基于二種算法(LiR、SGDR)對(duì)Boston(波士頓房?jī)r(jià))數(shù)據(jù)集(506,13+1)進(jìn)行價(jià)格回歸預(yù)測(cè)并對(duì)比各自性能

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目錄

輸出結(jié)果

設(shè)計(jì)思路

核心代碼

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輸出結(jié)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):- 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. 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. '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. **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)

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設(shè)計(jì)思路

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核心代碼

from sklearn.linear_model import LinearRegression lr = 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)

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總結(jié)

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