Boston Housing Prices: Regression

Overview

This dataset concerns housing values in Boston suburbs. It’s based on the “Boston Housing Dataset” from University of California, Irvine, which in turn was taken from the StatLib library maintained at Carnegie Mellon University.

The target is medv: median value of owner-occupied homes in terms of thousands of dollars ($1000s).

Features:

  1. crim: per-capita crime rate by town.
  2. zn: proportion of residential land zoned for lots over 25,000 sq.ft.
  3. indus: proportion of non-retail business acres per town.
  4. chas: Charles River dummy variable (=1 if tract bounds river; 0 otherwise)
  5. nox: nitric oxides concentration (parts per 10 million)
  6. rm: average number of rooms per dwelling.
  7. age: proportion of owner-occupied units built prior to 1940.
  8. dis: weighted distances to five Boston employment centres.
  9. rad: index of accessibility to radial highways.
  10. tax: full-value property-tax rate per $10,000.
  11. ptratio: pupil-teacher ratio by town.
  12. b: 1000(Bk-0.63)^2 where Bk is the proportion of black people by town.
  13. lsat: percent lower status of the population.
  14. medv: median value of owner-occupied homes in terms of thousands of dollars ($1000s).

Download

Download: Boston-Housing.zip (19 KB)

  • md5sum:

    5306de665616e7d76e98ea8d98ffd4b2

  • sha256sum:

    e029e7695a87910c26861180d77c95db306ccbc01c0decfacedbf91e38c077c5


Setup

  1. After downloading, unzip Boston-Housing.zip

    unzip Boston-Housing.zip

  2. If you’re using a jar file, move it into the Boston-Housing directory: mv (BoostSRL jar file) Boston-Housing/
    mv (auc jar file) Boston-Housing/

  3. For learning/inference, full explanations are available on the “Regression Tutorial”. Commands are also listed below.

  • Learning:

java -cp BoostSRL.jar edu.wisc.cs.will.Boosting.Regression.RunBoostedRegressionTrees -reg -l -train train/ -target medv -trees 20

  • Inference:

java -cp BoostSRL.jar edu.wisc.cs.will.Boosting.Regression.RunBoostedRegressionTrees -i -test test/ -aucJarPath . -target medv -model train/models/ -trees 20


Modes

Notice that since the values have been discretized, we can treat the values as constants and therefore we can use an octothorpe (#) in the modes file.

mode: crim(+id,#varsrim).
mode: zn(+id,#varzn).
mode: indus(+id,#varindus).
mode: chas(+id,#varchas).
mode: nox(+id,#varnox).
mode: rm(+id,#varrm).
mode: age(+id,#varage).
mode: dis(+id,#vardis).
mode: rad(+id,#varrad).
mode: tax(+id,#vartax).
mode: ptratio(+id,#varptrat).
mode: b(+id,#varb).
mode: lstat(+id,#varlstat).
mode: medv(+id).

Updated: