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sklearn.impute._base.SimpleImputer

sklearn.impute._base.SimpleImputer

Visibility: public Uploaded 31-03-2020 by Nicolas Hug sklearn==0.23.dev0 numpy>=1.6.1 scipy>=0.9 0 runs
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  • openml-python python scikit-learn sklearn sklearn_0.23.dev0
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Imputation transformer for completing missing values.

Parameters

add_indicatorIf True, a :class:`MissingIndicator` transform will stack onto output of the imputer's transform. This allows a predictive estimator to account for missingness despite imputation. If a feature has no missing values at fit/train time, the feature won't appear on the missing indicator even if there are missing values at transform/test time.default: false
copyIf True, a copy of X will be created. If False, imputation will be done in-place whenever possible. Note that, in the following cases, a new copy will always be made, even if `copy=False`: - If X is not an array of floating values; - If X is encoded as a CSR matrix; - If add_indicator=Truedefault: true
fill_valueWhen strategy == "constant", fill_value is used to replace all occurrences of missing_values If left to the default, fill_value will be 0 when imputing numerical data and "missing_value" for strings or object data typesdefault: null
missing_valuesThe placeholder for the missing values. All occurrences of `missing_values` will be imputeddefault: NaN
strategyThe imputation strategy - If "mean", then replace missing values using the mean along each column. Can only be used with numeric data - If "median", then replace missing values using the median along each column. Can only be used with numeric data - If "most_frequent", then replace missing using the most frequent value along each column. Can be used with strings or numeric data - If "constant", then replace missing values with fill_value. Can be used with strings or numeric data .. versionadded:: 0.20 strategy="constant" for fixed value imputationdefault: "mean"
verboseControls the verbosity of the imputerdefault: 0

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