68 lines
1.7 KiB
Markdown
68 lines
1.7 KiB
Markdown
# Imputation missing values
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For various reasons, many real world datasets contain missing values, often encoded as blanks, NaNs or other placeholders.
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To solve this problem you can use the `Imputer` class.
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## Constructor Parameters
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* $missingValue (mixed) - this value will be replaced (default null)
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* $strategy (Strategy) - imputation strategy (read to use: MeanStrategy, MedianStrategy, MostFrequentStrategy)
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* $axis (int) - axis for strategy, Imputer::AXIS_COLUMN or Imputer::AXIS_ROW
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* $samples (array) - array of samples to train
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```
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$imputer = new Imputer(null, new MeanStrategy(), Imputer::AXIS_COLUMN);
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$imputer = new Imputer(null, new MedianStrategy(), Imputer::AXIS_ROW);
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```
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## Strategy
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* MeanStrategy - replace missing values using the mean along the axis
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* MedianStrategy - replace missing values using the median along the axis
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* MostFrequentStrategy - replace missing using the most frequent value along the axis
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## Example of use
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```
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use Phpml\Preprocessing\Imputer;
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use Phpml\Preprocessing\Imputer\Strategy\MeanStrategy;
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$data = [
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[1, null, 3, 4],
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[4, 3, 2, 1],
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[null, 6, 7, 8],
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[8, 7, null, 5],
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];
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$imputer = new Imputer(null, new MeanStrategy(), Imputer::AXIS_COLUMN);
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$imputer->fit($data);
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$imputer->transform($data);
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/*
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$data = [
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[1, 5.33, 3, 4],
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[4, 3, 2, 1],
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[4.33, 6, 7, 8],
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[8, 7, 4, 5],
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];
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*/
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```
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You can also use the `$samples` constructor parameter instead of the `fit` method:
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```
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use Phpml\Preprocessing\Imputer;
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use Phpml\Preprocessing\Imputer\Strategy\MeanStrategy;
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$data = [
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[1, null, 3, 4],
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[4, 3, 2, 1],
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[null, 6, 7, 8],
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[8, 7, null, 5],
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];
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$imputer = new Imputer(null, new MeanStrategy(), Imputer::AXIS_COLUMN, $data);
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$imputer->transform($data);
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```
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