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import time | |
start_time = time.time() | |
import numpy as np | |
import pandas as pd | |
from sklearn.ensemble import RandomForestRegressor | |
#from sklearn import pipeline, model_selection | |
from sklearn import pipeline, grid_search | |
#from sklearn.feature_extraction import DictVectorizer | |
from sklearn.base import BaseEstimator, TransformerMixin |
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> tuning.rg(ntrn,lambda,10,0.7) | |
[1] "=== cross validation error estimation ===" | |
[1] "lambda= 0.0001 : error= 11.0574771599181 +- 1.5911393576281" | |
[1] "lambda= 0.001 : error= 11.0554573634712 +- 1.59159985887222" | |
[1] "lambda= 0.01 : error= 11.0377334394576 +- 1.59698319488323" | |
[1] "lambda= 0.1 : error= 11.0537037442444 +- 1.70124419652984" | |
[1] "lambda= 1 : error= 14.7438810915394 +- 2.85190527401637" | |
[1] "lambda= 10 : error= 24.6020816723033 +- 5.47557805542126" | |
[1] "lambda= 100 : error= 40.0103673810083 +- 9.25347219989225" | |
[1] "lambda= 1000 : error= 56.4994813733851 +- 9.68175560688852" |
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> tuning.svm("normalized data",ntrn,cvec,10,0.7) | |
[1] "normalized data" | |
[1] "#########linear SVM#################" | |
[1] "parameter= 1 the best cost= 3.2 the error = 13.0911820319358" | |
[1] "########polynomial kernel SVM##########" | |
[1] "parameter= 2 the best cost= 1 the error = 36.0239737549915" | |
[1] "parameter= 3 the best cost= 1 the error = 15.5345486240454" | |
[1] "parameter= 4 the best cost= 0.32 the error = 33.5509708662857" | |
[1] "parameter= 5 the best cost= 0.32 the error = 41.9638713171909" | |
[1] "parameter= 6 the best cost= 0.032 the error = 45.8911349089365" |
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> tuning.nn(trn,neuron,10,0.7) | |
[1] "=== cross validation error estimation ===" | |
[1] "depth= 1 : error= 70.9794189514517 +- 15.904800477383" | |
[1] "depth= 2 : error= 70.9722170232778 +- 15.8954005190479" | |
[1] "depth= 3 : error= 71.0124803004767 +- 15.8597654828364" | |
[1] "depth= 4 : error= 70.5867870706421 +- 15.818947243105" | |
[1] "depth= 5 : error= 70.7432130613939 +- 15.6752259506747" | |
[1] "depth= 6 : error= 71.2585730501765 +- 16.1677503694949" | |
[1] "depth= 7 : error= 71.0870255049561 +- 16.0324643862916" | |
[1] "depth= 8 : error= 71.0129340109873 +- 15.9439431180934" |
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> tuning.rf(trn,mtry,20,0.7) | |
[1] "=== cross validation error estimation ===" | |
[1] "mtry= 2 : error= 9.89178214242082 +- 4.06175518360904" | |
[1] "mtry= 4 : error= 7.7269900435814 +- 2.53016370451501" | |
[1] "mtry= 6 : error= 7.48214012711454 +- 1.98854233231896" | |
[1] "mtry= 8 : error= 7.53741415199526 +- 1.78742904316897" | |
[1] "mtry= 10 : error= 7.92473002002763 +- 1.88953063618415" | |
[1] "mtry= 12 : error= 8.37506100241071 +- 1.9922333938174" | |
[1] "mtry= 14 : error= 8.61657448209067 +- 2.04644195003867" | |
[1] "mtry= 16 : error= 8.59454798339517 +- 2.05803385159497" |
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> depth=c(1,2,3,4,5) | |
> tuning.gbm(trn,depth,20,0.7) | |
[1] "=== cross validation error estimation ===" | |
[1] "depth= 1 : error= 12.2088907005888 +- 2.63624188615258" | |
[1] "depth= 2 : error= 9.99951434560433 +- 2.30181005481302" | |
[1] "depth= 3 : error= 9.39671136126591 +- 2.19944247921552" | |
[1] "depth= 4 : error= 9.14194019449996 +- 2.16337261929302" | |
[1] "depth= 5 : error= 8.97911772123462 +- 2.18464631653844" | |
[1] 5 | |
> #normalized data |