Hi, it feels like you want to use a ColumnTransformer that can apply different preprocessing to different columns, see e.g. this example: https://scikit-learn.org/stable/auto_examples/miscellaneous/plot_pipeline_display.html#displaying-a-complex-pipeline-chaining-a-column-transformer
You can use 'passthrough' for the columns you don't want to change. Cheers, Loïc > Hi, > > I have a mixture of table data and intermediate vectors from another model, > which don't seem to scale productively. The fact that > MinMaxScaler seems to do all features in X makes me wonder if/how people > train with such mixed data. > > The easy approaches seem to be either scale the db data and then combine with > the vectors, or just scale the db columns in place 'by hand'. > > Otherwise, I might consider adding a column-list option to the API. > > I suspect I'm just missing something important, since I wandered in following > this purely-tabular example, which seemed good before adding > ML-derived vectors: > > https://www.kaggle.com/code/carlmcbrideellis/tabular-classification-with-neural-networks-keras > > Any advice or more-appropriate example to follow would be great. > > Thanks, > > Bill > > -- _______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
