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"""
one_class_svm.py
This module defines a One-Class SVM model for anomaly detection.
One-Class SVM identifies a decision boundary that separates normal data points from potential outliers
in a high-dimensional feature space.
Key Features:
- Effective for detecting anomalies in high-dimensional datasets.
- Flexible kernel options for nonlinear decision boundaries.
- Suitable for datasets with a small proportion of outliers.
Parameters:
- kernel (str): Specifies the kernel type used in the algorithm.
- Common options: 'linear', 'poly', 'rbf' (default), and 'sigmoid'.
- gamma (str or float): Kernel coefficient. Determines the influence of each sample.
- Default: 'scale' (1 / (n_features * X.var())).
- nu (float): Approximate fraction of outliers in the dataset.
- Must be in the range (0, 1]. Default: 0.05 (5% of data considered outliers).
Default Configuration:
- kernel='rbf': Radial Basis Function for nonlinear separation.
- gamma='scale': Automatically adjusts kernel influence based on dataset features.
- nu=0.05: Assumes approximately 5% of data points are outliers.
"""
from sklearn.svm import OneClassSVM
# Define the One-Class SVM estimator
estimator = OneClassSVM(
kernel='rbf', # Radial Basis Function kernel for nonlinear boundaries
gamma='scale', # Adjusts kernel influence based on dataset variance
nu=0.05 # Assumes 5% of the data are outliers
)
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