Comments (4)
wdm0006
commented on July 4, 2024
How large in bytes is the dataset being passed in? If it's a pandas dataframe you can use df.info(memory_usage=True) to find out.
Also what are you using for n_components?
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stevewyl
commented on July 4, 2024
The dataset is just 170MB, I set the n_components as 500.
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wdm0006
commented on July 4, 2024
How many columns are there in the original dataset, and how many of those are being encoded with the HashingEncoder (by default it'd be all).
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wdm0006
commented on July 4, 2024
Closing this, feel free to re-open if you're still having issues.
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Related Issues (20)
- Decoding Function for HashingEncoder
- Hash Encoding Outputting High Collision Output HOT 4
- Simplify BaseEncoder mapping HOT 1
- Combining Glmm Encoder with the PolynominalWrapper for Mutlinomial-classification HOT 3
- Sklearn pipeline compatibility and pandas dependencies
- Quadratic time intersection on Pandas categories HOT 2
- get_feature_names_out is incompatible with sklearn estimators and eli5, consequently HOT 3
- Equivalent method to sklearn's partial_fit? HOT 1
- CountEncoder incorrectly counts Timestamp columns HOT 3
- Target encoding categories with a single training example HOT 1
- DOC: one of the source links is dead HOT 1
- Missing text in documentation HOT 2
- Support Pandas 2.1 HOT 1
- Feature Request: Count-Based Target Encoder (Dracula)? HOT 1
- Pandas' string columns are not recognized HOT 3
- Pandas copy-on-write doesn't work properly HOT 2
- pd.NA should behave as np.nan HOT 5
- Multidimensional/composite target encoding HOT 4
- FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. HOT 2
- Support for Spark HOT 1
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