• other library_name: autogluon license: mit metrics:
• name: accuracy type: accuracy value: 1.0
• name: weighted f1 type: f1 value: 1.0 tags:
• autogluon
• tabular
• automl
• binary-classification

AutoGluon Tabular Predictor for Receiver Stats

Training Details

• Problem Type: binary
• Trained Models: AutoGluon automatically trained and ensembled a stack of models.
• Training Time: 222.63 seconds

Best Model: WeightedEnsemble_L2

• L1 Models (Base Models): This is the first layer of models. It includes a variety of individual machine learning models, like LightGBM, CatBoost, and others, each trained directly on the original dataset's features. The predictions of these L1 models are saved and used as the "features" for the next level.
• L2 Models (Meta-Models): The WeightedEnsemble_L2 is the meta-model at this second stacking level. It doesn't train on the original data features. Instead, it uses the out-of-fold predictions from all the L1 models as its input. Its job is to find the optimal way to combine these predictions to create a final, superior prediction.

Performance on Original Data

• Accuracy: 1.0000
• Weighted F1: 1.0000

Best Model Information

• Problem Type: binary
• Evaluation Metric: accuracy
• Validation Score: 0.9968
• Fit Time: 0.0756 seconds

Best Model Hyperparameters:

use_orig_features: False
valid_stacker: True
max_base_models: 0
max_base_models_per_type: auto
save_bag_folds: True
stratify: auto
bin: auto
n_bins: None

Child Model Information (if applicable):

• S1F1:
  • Model Type: GreedyWeightedEnsembleModel
  • Fit Time: 0.0756 seconds
  • Fit Hyperparameters:
    • ensemble_size: 1

Model Leaderboard

How to Use