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Support Vector Machine
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Support Vector Machine classical method implementation.

This section contains the Support Vector Machine (SVM) implementation for classification tasks. SVM is a supervised learning algorithm that finds a hyperplane to separate data points of different classes with maximum margin.

.. automodule:: TALENT.model.classical_methods.svm
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   :undoc-members:
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.. class:: SVMMethod
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   Support Vector Machine method for classification tasks.
   
   **Key Features:**
   
   - Uses sklearn's SVC for classification
   - Finds optimal hyperplane for class separation
   - Supports both binary and multiclass classification
   - Automatically handles data preprocessing including normalization and encoding
   - Saves trained model to pickle file for later use
   - Provides probability predictions
   
   **Algorithm:**
   
   SVM is a supervised learning algorithm that finds a hyperplane to separate data points of different classes with maximum margin. It can handle both linear and non-linear classification using kernel functions.

   .. method:: __init__(args, is_regression)
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      Initialize the SVM method.
      
      **Parameters:**
      
      * **args** (*object*) -- Configuration arguments containing model settings
      * **is_regression** (*bool*) -- Whether the task is regression (True) or classification (False)

   .. method:: construct_model(model_config=None)
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      Construct the SVM model instance.
      
      **Parameters:**
      
      * **model_config** (*dict, optional*) -- Model configuration parameters for SVM
      
      **Model Creation:**
      
      - Creates `SVC` classifier
      - Configures parameters like kernel, C, gamma, etc.

   .. method:: fit(data, info, train=True, config=None)
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      Train the SVM model on the provided data.
      
      **Parameters:**
      
      * **data** (*tuple*) -- Tuple containing (N, C, y) where N is numerical features, C is categorical features, y is labels
      * **info** (*dict*) -- Dataset information
      * **train** (*bool, default=True*) -- Whether to train the model or just load from checkpoint
      * **config** (*dict, optional*) -- Additional configuration parameters
      
      **Returns:**
      
      * **time_cost** (*float*) -- Training time in seconds
      
      **Training Process:**
      
      1. **Data Preprocessing:** Handles missing values, categorical encoding, normalization
      2. **Model Training:** Fits the SVM model with optimal hyperplane
      3. **Model Saving:** Saves the trained model to disk for later use

   .. method:: predict(data, info, model_name)
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      Make predictions using the trained SVM model.
      
      **Parameters:**
      
      * **data** (*tuple*) -- Tuple containing (N, C, y) where N is numerical features, C is categorical features, y is labels
      * **info** (*dict*) -- Dataset information
      * **model_name** (*str*) -- Name of the model for saving/loading
      
      **Returns:**
      
      * **test_logit** (*array-like*) -- Test predictions (probabilities for classification)
      
      **Prediction Process:**
      
      1. **Data Preprocessing:** Applies same preprocessing as training data
      2. **Model Loading:** Loads the trained SVM model
      3. **Prediction:** Generates probability predictions
      4. **Output:** Returns probabilities for classification

**Evaluation Metrics:**

- **For classification:** returns Accuracy, Avg_Precision, Avg_Recall, F1 metrics

**References:**

``[1] Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273-297.``