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python vowpalwabbit regression for big data files with FEARTURES INTERACTION

python vowpalwabbit regression for big data files with FEARTURES INTERACTION

1 ridge

2 lasso

3 quantile for both ridge and lasso !

FEATURES BOTH CATEGORICAL AND CONTINUES

IMPORTANT to HAVE INTERACTION BETWEEN CATEGORICAL FEARTURES : SECOND ORDER AND THIRED ORDER

like

from vowpalwabbit.sklearn_vw import VW, VWClassifier, VWRegressor

vw_squared = VWRegressor(loss_function='squared' , normalized = True, interactions = 'abc')

but better to use

from vowpalwabbit import pyvw

for big data like

us-used-cars-dataset 9 GB 3ml rows 66 features predict price

[login to view URL]

but start you can from

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all calculations done in vowpalwabbit python including one hot for categorical data (not scikit learn one hot)

data has both categorical and continues features

code starter

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[login to view URL]:~:text=Use%20chunksize%20to%20read%20a,be%20read%20in%20per%20chunk.

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vw [login to view URL] -f [login to view URL] –binary –passes 20 -c -q ff –sgd –l1

0.00000001 –l2 0.0000001 –learning_rate 0.5 –loss_function logistic

[login to view URL]

test3 <- c("-t", [login to view URL]("test", "train-sets", "[login to view URL]", package="RVowpalWabbit"),

"-f", [login to view URL](tempdir(), "[login to view URL]"),

"--cache_file", [login to view URL](tempdir(), "[login to view URL]"))

also [login to view URL] many example for VW

maybe >>> from [login to view URL] import DFtoVW

>>> import pandas as pd

>>> df = [login to view URL]({"y": [1], "x": [2]})

>>> conv = DFtoVW.from_colnames(y="y", x="x", df=df)

>>> conv.convert_df()

['1 | x:2']

Habilidades: Python, Machine Learning (ML), Inteligência Artificial

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Acerca do Empregador:
( 6 comentários ) Toronto, Canada

ID do Projeto: #31604230

Concedido a:

akrej

Hello. I have experience with ML in general and with Python ML stack. I would love to implement regression models in Vowpal Wabbit for you, especially if it's for large data sets :-) Regards Andrzej

$25 USD em 4 dias
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