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