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Hello guys. First I want to thank you for the work you are doing here. I'm here to suggest an improvement in the Preparing Data part of the MLJ. I missed some functionality of scikit-learn.
Topics covered in the MLJ documentation are:
Common data preprocessing workflows
Scientific type coercion
Data transformations
Scientific type coercion
Data transformation
In scikit-learn they are:
Standardization, or mean removal and variance scaling
Scaling features to a range
Scaling sparse data
Scaling data with outliers
Centering kernel matrices
Non-linear transformation
Mapping to a Uniform distribution
Mapping to a Gaussian distribution
Normalization
Encoding categorical features
Infrequent categories
Discretization
K-bin discretization
Feature binarization
Imputation of missing values
Generating polynomial features
Polynomial features
Spline transformer
Custom transformers
The text was updated successfully, but these errors were encountered:
Hello guys. First I want to thank you for the work you are doing here. I'm here to suggest an improvement in the Preparing Data part of the MLJ. I missed some functionality of scikit-learn.
Topics covered in the MLJ documentation are:
In scikit-learn they are:
The text was updated successfully, but these errors were encountered: