What does QC mean in UNCLASSIFIED

QC is a supervised machine learning algorithm used for binary classification problems. It creates a quadratic decision boundary, which is a curved surface in the feature space, to separate the two classes. QC models complex relationships between features and the target variable.

Unlike linear classifiers (e.g., logistic regression), which use a straight line as the decision boundary, QC allows for non-linear relationships. It captures quadratic interactions and curvature, providing greater flexibility in modeling complex data patterns.

QC offers several advantages:

• Non-linearity: Handles complex relationships between features and the target variable.
• Flexibility: Captures quadratic interactions and curvature, allowing for more accurate modeling.
• Robustness: Less prone to overfitting compared to other non-linear classifiers.

QC is suitable for situations where:

• The data exhibits non-linear relationships between features and the target variable.
• The decision boundary is expected to be curved.
• Other linear classifiers fail to capture the complexity of the data.

QC has some limitations:

• Computational complexity: The training process can be computationally intensive, especially for large datasets.
• Interpretability: The quadratic decision boundary can be difficult to interpret and visualize.
• Regularization: Requires careful regularization to prevent overfitting.

The choice of kernel is crucial in QC. Common kernels include:

• Gaussian kernel: Suitable for data with non-linear relationships and high dimensionality.
• Polynomial kernel: Captures polynomial interactions of features.
• Sigmoid kernel: Used in support vector machines for non-linear classification.