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Optimizing SVM Parameters: Since we aim to use an SVM model, we could contribute by designing a robust hyperparameter tuning strategy using grid search, random search, or Bayesian optimization to reach higher accuracy.
Implementing Advanced Models: Beyond SVM, we might add models like (e.g., Random Forests or Gradient Boosting) or even deep learning models like CNNs, which can improve predictive accuracy if data is adequate.
The text was updated successfully, but these errors were encountered:
Optimizing SVM Parameters: Since we aim to use an SVM model, we could contribute by designing a robust hyperparameter tuning strategy using grid search, random search, or Bayesian optimization to reach higher accuracy.
Implementing Advanced Models: Beyond SVM, we might add models like (e.g., Random Forests or Gradient Boosting) or even deep learning models like CNNs, which can improve predictive accuracy if data is adequate.
The text was updated successfully, but these errors were encountered: