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This model is a decision tree classifier trained to predict obesity levels based on demographic, lifestyle, and diet-related features. The dataset includes variables including age, height, weight, caloric food intake, physical activity, water consumption, smoking behavior, and transportation habits. The target label is the obesity category, which includes seven classes ranging from Insufficient_Weight to Obesity_Type_III. The decision tree originally had 12 layers, which was cut down (pruned) to improve interpretability and reduce overfitting.
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## Intended uses & limitations
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[More Information Needed]
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## Training Procedure
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[More Information Needed]
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### Hyperparameters
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This model is a decision tree classifier trained to predict obesity levels based on demographic, lifestyle, and diet-related features. The dataset includes variables including age, height, weight, caloric food intake, physical activity, water consumption, smoking behavior, and transportation habits. The target label is the obesity category, which includes seven classes ranging from Insufficient_Weight to Obesity_Type_III. The decision tree originally had 12 layers, which was cut down (pruned) to improve interpretability and reduce overfitting.
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### Hyperparameters
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