MLPER-06: Explore alternatives for performance improvement

Use more data to broaden the statistical range and improve the success metric of the model.

Apply feature engineering to extract important signals in the data for the model.

Make alternative algorithm selections for an optimal fit to the specifics of the data.

Ensemble methods that combine the different advantages of multiple models.

Tune the hyperparameters for a given algorithm to calibrate the model for the data.

Use Amazon SageMaker AI Experiments to optimize algorithms and features -Begin with a simple architecture, obvious features, and a simple algorithm to establish a baseline. Amazon SageMaker AI provides built-in algorithms for developing a baseline model. Use Amazon SageMaker AI Experiments to organize, track, compare, and evaluate your machine learning experiments. Test different algorithms with increasing complexity to observe performance. Combine models into an ensemble to increase accuracy, but consider the potential loss of efficiency as a trade-off. Refine the features by selection and modify parameters to optimize model performance. Tune the model’s hyperparameters to optimize performance using Amazon SageMaker AI Hyperparameter Optimization to automate the search.

Improving Model Accuracy

Evaluating ML Models

Feature Processing with Spark ML and Scikit-learn

Perform Automatic Model Tuning with SageMaker AI

Amazon SageMaker AI Experiments: Track and Compare Tutorial

Running multiple HPO jobs in parallel on Amazon SageMaker AI

Optimizing portfolio value with Amazon SageMaker AI automatic model tuning

Utilizing XGBoost training reports to improve your models

Tune your ML models to the highest accuracy with automatic model tuning

Organize, Track, and Evaluate ML Training Runs With Amazon SageMaker AI Experiments

Feature Engineering Immersion Day Workshop

Improving Forecast Accuracy with Machine Learning

Ensemble Predictions From Multiple Models