ml_feature_encoder 0.1.0
ml_feature_encoder: ^0.1.0 copied to clipboard
A Dart package for encoding categorical variables using Label Encoding and One-Hot Encoding techniques. Ideal for machine learning preprocessing.
π§ ml_feature_encoder #
A lightweight and pure Dart library for encoding categorical variables using Label Encoding and One-Hot Encoding β essential for machine learning data preprocessing.
β¨ Features #
- β Label Encoding for string/categorical data
- β One-Hot Encoding with inverse transform
- β
handleUnknown
support for unseen values - β
Clean API:
fit()
,transform()
,inverseTransform()
- β Inspired by scikit-learn
- β Fully tested and documented
π Installation #
Add the following to your pubspec.yaml
:
dependencies:
ml_feature_encoder: ^0.1.0
Then run:
dart pub get
π¦ Usage #
import 'package:ml_feature_encoder/encoders/label_encoder.dart';
import 'package:ml_feature_encoder/encoders/one_hot_encoder.dart';
void main() {
// LabelEncoder example
final labelEncoder = LabelEncoder();
labelEncoder.fit(['cat', 'dog', 'fish']);
final encoded = labelEncoder.transform(['dog', 'cat']);
final decoded = labelEncoder.inverseTransform(encoded);
print('Encoded: $encoded');
print('Decoded: $decoded');
// OneHotEncoder example
final oneHotEncoder = OneHotEncoder(handleUnknown: true);
oneHotEncoder.fit(['red', 'green', 'blue']);
final oneHot = oneHotEncoder.transform(['green', 'yellow']);
print('One-hot: $oneHot');
}
β Example Output #
Encoded: [1, 0]
Decoded: [dog, cat]
One-hot: [
[0, 1, 0],
[0, 0, 0] // unknown 'yellow'
]
π Directory Structure #
lib/
βββ encoders/
β βββ label_encoder.dart
β βββ one_hot_encoder.dart
βββ ml_feature_encoder.dart
test/
βββ label_encoder_test.dart
βββ one_hot_encoder_test.dart
example/
βββ main.dart
π‘ License #
MIT Β© Mehmet Γelik
π£ Contributions #
Issues, suggestions, and PRs are welcome! Feel free to improve this library or report a bug.