SVC stands for Support Vector Classification, which refers to a supervised machine learning algorithm used for binary classification tasks. It is a type of Support Vector Machine (SVM) algorithm that analyzes data and separates it into two classes, making it useful for tasks like fraud detection, email filtering, or image classification.
The SVC algorithm aims to find an optimal hyperplane that best separates the data points of different classes while maximizing the margin, or distance, between classes. The hyperplane is defined by support vectors, which are the data points closest to the decision boundary. These vectors play a crucial role in determining the classifier's accuracy.
When applied to a binary classification problem, SVC assigns new data points to one of the two classes based on which side of the hyperplane they fall. It determines this by calculating the distance from the data point to the hyperplane and comparing it to a threshold value.
To build an SVC model, the algorithm requires a training dataset where each data point is labeled with its corresponding class. By using various optimization techniques and mathematical algorithms, SVC finds the hyperplane that minimizes classification error and maximizes the margin between classes.
With its ability to handle large feature spaces, the SVC algorithm is known for its flexibility and effectiveness in handling both linearly and non-linearly separable datasets. By mapping the data into higher-dimensional spaces, SVC can capture complex training patterns, improving classification accuracy. However, for large datasets, SVC can be computationally intensive and might require careful parameter tuning to prevent overfitting or underfitting.
