sklearn StandardScaler vs dask StandardScaler.

[Arunes007]

I am getting different results from sklearn StandardScaler and dask StandardScaler.

scaler_sk = sklearn.preprocessing.StandardScaler()
scaler_d = dask_ml.preprocessing.StandardScaler()

scaler_sk.fit(df_pd[["SUMMESSAGECOUNT"]])
scaler_d.fit(df_dask[["SUMMESSAGECOUNT"]])

Dask scaler

scaler_d.mean_[0], scaler_d.var_[0]
output: (19.157653421114507, 47431.17794342375)

Sklearn Scaler

scaler_sk.mean_[0], scaler_sk.var_[0]
output: (19.157653421114507, 47431.17794342373)

I know the difference is negligible. But it is influencing my model training on prophet. Could you please suggest any way to make them identical without using compute().

[TomAugspurger]

I think that floating point inaccuracies are just a fact of life when you’re doing things in chunks, at least with the algorithms that dask.array uses today. I don’t think there’s anything we can do in dask-ml to address that (but maybe check the source to be sure).

On Dec 1, 2023, at 5:35 AM, Arunesh Singh @.***> wrote:

I am getting different results from sklearn StandardScaler and dask StandardScaler.

scaler_sk = sklearn.preprocessing.StandardScaler() scaler_d = dask_ml.preprocessing.StandardScaler()

scaler_sk.fit(df_pd[["SUMMESSAGECOUNT"]]) scaler_d.fit(df_dask[["SUMMESSAGECOUNT"]]) Dask scaler

scaler_d.mean_[0], scaler_d.var_[0] output: (19.157653421114507, 47431.17794342375) Sklearn Scaler

scaler_sk.mean_[0], scaler_sk.var_[0] output: (19.157653421114507, 47431.17794342373) I know the difference is negligible. But it is influencing my model training on prophet. Could you please suggest any way to make them identical without using compute().

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