AnLOF 1.0.1

Automatic Outliers Handler Library

AnLOF

AnLOF is a machine learning utility library built to automatically handle outliers effectively.

AnLOF uses multiple outlier-handling techniques and automatically selects the best-performing method.

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Outlier Handling Methods Used

• IQR Score
• Z-score
• Winsorization
• Median Imputation
• Mean Imputation
• Isolation Forest
• Box-Cox Transformation
• k-Nearest Neighbors
• XGBRegressor
• LGBMRegressor
• CatBoostRegressor
• StandardScaler
• RobustScaler
• MinMaxScaler
• Log Transformation
• Quantile Normalization

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Features

• Automatic outlier detection and handling
• Multiple preprocessing strategies
• Model-based evaluation
• Selects the best method based on the chosen metric
• Returns your dataset with the best preprocessing method applied

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Hyperparameters

• X_train : Your training feature set

• X_val : Your validation feature set

• y_train : Your training target values

• y_val : Your validation target values

• features : Features that contain outliers

  Note: This should NOT include all features in your dataset, only those suspected of containing outliers.

• base_model : The model used to evaluate the performance of each preprocessing method.

• metric : The evaluation metric used to compare performance.

• higher_is_better :

  • True for metrics where higher values are better (e.g. accuracy_score, f1_score)
  • False for metrics where lower values are better (e.g. mean_squared_error)

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Author

Author

This project is developed and maintained by MurtazaA2010.

For any questions or support: Email : [e-mail] Web : Murtaza Abdullah

Installation

pip install AnLOF

```python
from AnLOF.AnLOF_module import AnLOF
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error

anlof = AnLOF(
    X_train,
    X_val,
    y_train,
    y_val,
    features=["feature1", "feature2"],  # features containing outliers
    base_model=LinearRegression,
    metric=mean_squared_error,
    higher_is_better=False
)

best_X_train, best_X_val, best_method, performance_df = anlof.forward()

# best_method : the method with the best score
# performance_df : contains the performance of all the methods

print("Best method:", best_method)

print("Best X_train:")
print(best_X_train.head())

print("Best X_val:")
print(best_X_val.head())

print("Performance comparison:")
print(performance_df)