vowpalwabbit python ridge regression with FEARTURES INTERACTION

vowpalwabbit python ridge regression with FEARTURES INTERACTION

ridge regression

FEATURES BOTH CATEGORICAL AND CONTINUES

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

create namespace

one for categorical and one for continues features

code interactoins between all categorical values

like

from vowpalwabbit.sklearn_vw import VW, VWClassifier, VWRegressor

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

but better to use

from vowpalwabbit import pyvw

data

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make sure you code performance is better than baselines

train and predict by using train data file ( no split to train and test data sets)

PROVE YOUR CODE IS CORRECT

1

corerct perofrmance dynamics for paramters changing

2

good performance

3

code logic

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

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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']

Python Machine Learning (ML) Inteligência Artificial Estatísticas

ID do Projeto: #31779643

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