To avoid overfitting in Automated Machine Learning (AutoML), you can implement the following strategies:
Cross-Validation: Use k-fold cross-validation to evaluate model performance on multiple subsets of the data, ensuring the model generalizes well rather than memorizing the training set. AutoML platforms often automate this process.
Regularization: Apply L1 (Lasso) or L2 (Ridge) regularization to penalize overly complex models. AutoML tools typically include regularization options in their hyperparameter tuning.
Early Stopping: Monitor validation performance during training and halt the process if performance plateaus or degrades, preventing the model from over-optimizing on noise. Many AutoML frameworks support early stopping.
Data Augmentation: Increase training data diversity through techniques like rotation, flipping, or noise injection (for images, audio, or text). This helps the model generalize better.
Simplify Model Complexity: Limit the depth of decision trees, the number of layers in neural networks, or the number of features used. AutoML systems often include complexity constraints in their search space.
Ensemble Methods: Combine predictions from multiple models to reduce variance. AutoML platforms like Tencent Cloud’s TI-ONE can automatically generate and ensemble diverse models.
Use a Holdout Validation Set: Reserve a portion of the data solely for final evaluation to ensure the model’s performance is not biased by training data.
Example: If you’re using Tencent Cloud’s TI-ONE for AutoML, enable cross-validation, apply L2 regularization, and set early stopping criteria in the pipeline configuration. The platform will automatically optimize hyperparameters while mitigating overfitting.
For structured data, TI-ONE’s automated feature engineering and model selection can help balance complexity and performance. For unstructured data (e.g., images), use data augmentation techniques supported by the platform to expand the training set.