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df_orig) in our experiment predicting shoe length.US size, Shoe size (mm)) and encoded text features (type, color, and brand embeddings).maryzhang/hw1-24679-tabular-dataset from the Hugging Face Datasets library. Specifically, the augmented split (augmented_ds) was used for training and validation (via AutoGluon's internal splitting), and the original split (original_ds) was used for final testing/leaderboard evaluation.original_ds (corresponding to an RMSE of approximately 0.580 mm). This score represents the model's performance on unseen data.WeightedEnsemble_L2) might have a different validation score, but you chose to upload the specific LightGBMXT model based on its test set performance.TabularPredictor training run.1# You would typically load the entire predictor to use AutoGluon's best model selection,
2# but if you specifically need this LightGBMXT model:
3# You might need to load the full predictor directory first and then access the specific model.
4
5# Example (requires AutoGluon and potentially loading the full predictor first):
6# from autogluon.tabular import TabularPredictor
7# predictor_path = "/path/where/you/downloaded/this/repo" # Point to the downloaded repo directory
8# predictor = TabularPredictor.load(predictor_path)
9# lightgbmxt_model = predictor.get_model('LightGBMXT') # Access the specific model
10# predictions = lightgbmxt_model.predict(your_new_data)
11
12# A simpler approach might be to download the full predictor directory and load it
13# predictor = TabularPredictor.load("kaitongg/shoe-length-predictor-lightgbmxt") # If the full predictor was uploaded
14# predictions = predictor.predict(your_new_data)
15
16# Note: Loading just a single model like this might require careful handling of preprocessing
17# and feature engineering steps that were part of the original AutoGluon pipeline.
18# Loading the full predictor is generally recommended if it was uploaded.