Offline stageMLCopilotTask description T̃The task is to classify a brain tumor based on ampMRI scan. The dataset contains ~400k samples.Demonstrations ẼTask: Classify a lung tumor based on blood testreport. The dataset contains around 1k samples.Solution: Model is xgboost. Using small max depth.Task: Shoulder X-ray classification. Diagnose typeare A1, C1, D1. The dataset contains 500 samples.Solution: Using pretrained EfficientNet-B0. Finetunewith a low learning rate and high weight decay.Knowledge K̃1. CNN is commonly used for image datasets. Fortabular datasets, use decision tree algorithms.2. Use a low learning rate when finetuning on apretrained model. Use a high learning rate toconverge faster.3. For small datasets, control regularizationparameters like max depth to prevent overfit.Online stageExperience pool PE2. RetrieveExperienceKnowledge pool PKOffline Elicitation（with LLM)3. RetrieveKnowledge1. DescribetaskSolutionFinetune on a pretrained EfficientNet-B0 withlow learning rate
MLCopilot- Unleashing the Power of Large Language Models in Solving Machine Learning Tasks