ecopus/receiverstats-autolguon-predictor

datasets:

• 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

This model was trained using AutoGluon's TabularPredictor on the "Receiver Stats" dataset from the Hugging Face Hub. This model works to classify popular NFL wide-recievers as "Elite" or not based on an augmented set of season statistics. This was was validates on a set of real-world wide-reciever statistics.

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

WeightedEnsemble_L2 is a meta-model that combines predictions from other base models. The goal is to create a more accurate and robust final prediction than any single model alone.

AutoGluon builds a hierarchy of models called "stacking levels."

• 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

The following are the performance metrics on the original test dataset (df_orig). This represents the model's performance on unseen, real-world 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

This table shows the performance of the individual models and ensembles AutoGluon trained on the augmented data test set.

model	score_test	score_val	eval_metric	pred_time_test	pred_time_val	fit_time	pred_time_test_marginal	pred_time_val_marginal	fit_time_marginal	stack_level	can_infer	fit_order
LightGBM_BAG_L1	1	0.996795	accuracy	0.0182326	0.0151243	2.3435	0.0182326	0.0151243	2.3435	1	True	2
LightGBMLarge_BAG_L1	1	0.99359	accuracy	0.0185711	0.0163007	3.83537	0.0185711	0.0163007	3.83537	1	True	10
WeightedEnsemble_L2	1	0.996795	accuracy	0.0196931	0.0160172	2.41913	0.00146055	0.000892878	0.0756328	2	True	31
WeightedEnsemble_L3	1	0.996795	accuracy	0.0198486	0.0160413	2.42848	0.001616	0.000916958	0.0849795	3	True	53
LightGBM_r131_BAG_L1	1	0.99359	accuracy	0.0229213	0.0160203	2.52313	0.0229213	0.0160203	2.52313	1	True	12
LightGBM_r130_BAG_L1	1	0.983974	accuracy	0.0234201	0.0154045	2.29752	0.0234201	0.0154045	2.29752	1	True	24
XGBoost_BAG_L1	1	0.99359	accuracy	0.0628488	0.0438747	1.34741	0.0628488	0.0438747	1.34741	1	True	8
XGBoost_r89_BAG_L1	1	0.996795	accuracy	0.0642865	0.0316713	1.03006	0.0642865	0.0316713	1.03006	1	True	22
RandomForestGini_BAG_L1	1	0.987179	accuracy	0.0765729	0.133395	0.633404	0.0765729	0.133395	0.633404	1	True	3
XGBoost_r33_BAG_L1	1	0.99359	accuracy	0.0790606	0.0315175	1.54053	0.0790606	0.0315175	1.54053	1	True	16
RandomForestEntr_BAG_L1	1	0.987179	accuracy	0.0835617	0.115584	0.63149	0.0835617	0.115584	0.63149	1	True	4
RandomForest_r195_BAG_L1	1	0.99359	accuracy	0.0875499	0.139778	1.26689	0.0875499	0.139778	1.26689	1	True	19
LightGBMXT_BAG_L2	1	0.996795	accuracy	0.496401	0.48371	32.3345	0.0173769	0.0194232	2.37661	2	True	32
LightGBM_BAG_L2	1	0.99359	accuracy	0.49646	0.491807	33.5325	0.0174353	0.0275197	3.5746	2	True	33
LightGBM_r131_BAG_L2	1	0.99359	accuracy	0.497093	0.484313	32.7382	0.0180688	0.0200264	2.78029	2	True	43
LightGBM_r188_BAG_L2	1	0.99359	accuracy	0.497564	0.486396	33.2061	0.0185392	0.022109	3.24822	2	True	51
LightGBMLarge_BAG_L2	1	0.996795	accuracy	0.498554	0.484489	33.2319	0.0195298	0.0202019	3.27402	2	True	41
LightGBM_r96_BAG_L2	1	0.99359	accuracy	0.499495	0.488732	32.6217	0.0204709	0.0244451	2.66378	2	True	45
XGBoost_BAG_L2	1	0.99359	accuracy	0.540117	0.498556	30.7713	0.0610926	0.0342691	0.813397	2	True	39
RandomForest_r195_BAG_L2	1	0.99359	accuracy	0.553618	0.655855	31.4804	0.0745938	0.191568	1.52246	2	True	50
ExtraTreesGini_BAG_L2	1	0.99359	accuracy	0.553702	0.588537	30.6041	0.0746779	0.12425	0.64614	2	True	36
RandomForestEntr_BAG_L2	1	0.996795	accuracy	0.554335	0.582469	30.6311	0.0753102	0.118182	0.67319	2	True	35
ExtraTreesEntr_BAG_L2	1	0.99359	accuracy	0.554695	0.580956	30.607	0.0756705	0.116669	0.649115	2	True	37
ExtraTrees_r42_BAG_L2	1	0.99359	accuracy	0.554827	0.577228	30.6165	0.0758023	0.112941	0.658593	2	True	48
RandomForestGini_BAG_L2	1	0.99359	accuracy	0.556113	0.594949	30.5963	0.0770888	0.130661	0.638425	2	True	34
XGBoost_r33_BAG_L2	1	0.996795	accuracy	0.557637	0.497719	31.1206	0.0786128	0.0334322	1.16271	2	True	47
NeuralNetTorch_BAG_L2	1	0.99359	accuracy	0.558177	0.545795	37.6299	0.0791523	0.0815077	7.67201	2	True	40
NeuralNetFastAI_BAG_L2	1	0.996795	accuracy	0.564292	0.533021	33.2418	0.0852675	0.0687342	3.28393	2	True	38
NeuralNetTorch_r22_BAG_L2	1	0.99359	accuracy	0.564964	0.540373	36.0072	0.0859399	0.0760863	6.04927	2	True	46
NeuralNetFastAI_r191_BAG_L2	1	0.996795	accuracy	0.572105	0.538377	35.5212	0.0930803	0.0740898	5.56326	2	True	44
NeuralNetTorch_r79_BAG_L2	1	0.99359	accuracy	0.57672	0.552144	37.0812	0.0976958	0.0878572	7.12327	2	True	42
NeuralNetFastAI_r145_BAG_L2	1	0.996795	accuracy	0.581234	0.531907	33.7659	0.10221	0.06762	3.80796	2	True	52
NeuralNetFastAI_r102_BAG_L2	1	0.99359	accuracy	0.606663	0.552316	40.9467	0.127638	0.0880289	10.9887	2	True	49
LightGBMXT_BAG_L1	0.987179	0.980769	accuracy	0.0261004	0.0248475	3.3106	0.0261004	0.0248475	3.3106	1	True	1
XGBoost_r194_BAG_L1	0.987179	0.99359	accuracy	0.0529051	0.0212286	0.974106	0.0529051	0.0212286	0.974106	1	True	27
ExtraTrees_r172_BAG_L1	0.987179	0.951923	accuracy	0.0861583	0.118652	0.644855	0.0861583	0.118652	0.644855	1	True	28
LightGBM_r188_BAG_L1	0.974359	0.974359	accuracy	0.0304482	0.0173674	2.59352	0.0304482	0.0173674	2.59352	1	True	20
ExtraTrees_r42_BAG_L1	0.974359	0.961538	accuracy	0.0853848	0.119849	0.646125	0.0853848	0.119849	0.646125	1	True	17
ExtraTreesEntr_BAG_L1	0.961538	0.939103	accuracy	0.0842819	0.118352	0.690058	0.0842819	0.118352	0.690058	1	True	6
ExtraTreesGini_BAG_L1	0.961538	0.948718	accuracy	0.0864713	0.115839	0.628436	0.0864713	0.115839	0.628436	1	True	5
NeuralNetFastAI_r145_BAG_L1	0.961538	0.964744	accuracy	0.0907433	0.069463	7.14193	0.0907433	0.069463	7.14193	1	True	21
NeuralNetTorch_BAG_L1	0.948718	0.945513	accuracy	0.0804045	0.0681808	11.8957	0.0804045	0.0681808	11.8957	1	True	9
NeuralNetTorch_r86_BAG_L1	0.948718	0.942308	accuracy	0.0839164	0.0717986	10.5252	0.0839164	0.0717986	10.5252	1	True	25
NeuralNetTorch_r22_BAG_L1	0.948718	0.945513	accuracy	0.0851119	0.0708473	10.4364	0.0851119	0.0708473	10.4364	1	True	15
NeuralNetTorch_r79_BAG_L1	0.948718	0.951923	accuracy	0.0932202	0.0793908	10.3078	0.0932202	0.0793908	10.3078	1	True	11
NeuralNetTorch_r30_BAG_L1	0.948718	0.948718	accuracy	0.109228	0.0770526	13.1373	0.109228	0.0770526	13.1373	1	True	23
NeuralNetFastAI_BAG_L1	0.935897	0.961538	accuracy	0.087167	0.0731091	3.97873	0.087167	0.0731091	3.97873	1	True	7
NeuralNetFastAI_r11_BAG_L1	0.935897	0.958333	accuracy	0.0967791	0.0799751	8.92228	0.0967791	0.0799751	8.92228	1	True	26
NeuralNetFastAI_r103_BAG_L1	0.935897	0.948718	accuracy	0.100346	0.0640857	5.52301	0.100346	0.0640857	5.52301	1	True	29
NeuralNetFastAI_r191_BAG_L1	0.935897	0.958333	accuracy	0.104501	0.0720098	8.91329	0.104501	0.0720098	8.91329	1	True	13
NeuralNetFastAI_r102_BAG_L1	0.935897	0.967949	accuracy	0.13035	0.0784743	14.3636	0.13035	0.0784743	14.3636	1	True	18
LightGBM_r96_BAG_L1	0.923077	0.945513	accuracy	0.0355909	0.0173631	2.36029	0.0355909	0.0173631	2.36029	1	True	14
NeuralNetTorch_r14_BAG_L1	0.923077	0.942308	accuracy	0.0753639	0.0766191	6.80521	0.0753639	0.0766191	6.80521	1	True	30

How to Use

You can load this model and make predictions using AutoGluon Do not use this model to draw real-world conclusions, this model is for educational and instructional purposes only.