Automated Machine Learning (AutoML) evaluates model performance through a combination of metrics, cross-validation, and optimization techniques. Here's how it works:
Performance Metrics: AutoML uses predefined metrics based on the task type. For classification tasks, common metrics include accuracy, precision, recall, F1-score, and AUC-ROC. For regression tasks, metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared are used.
Cross-Validation: To ensure robustness, AutoML employs k-fold cross-validation. The dataset is split into k subsets, and the model is trained and evaluated k times, with each subset used as the test set once. This helps assess generalization performance.
Hyperparameter Tuning: AutoML optimizes hyperparameters (e.g., learning rate, number of layers) using techniques like Bayesian optimization or grid search. The goal is to find the best combination that maximizes performance metrics.
Benchmarking: AutoML compares multiple models (e.g., decision trees, neural networks) on the same dataset and selects the one with the best performance.
Example: In a fraud detection task, AutoML might evaluate logistic regression, random forest, and XGBoost models using AUC-ROC and F1-score. It would then pick the model with the highest AUC-ROC while ensuring low false positives.
For such tasks, Tencent Cloud offers TI-ONE, an AutoML platform that automates model training, evaluation, and deployment, supporting a wide range of machine learning algorithms and evaluation metrics.