Obtaining Irregular Learning Curves with HyberBand Tuned ANN model for Price Prediction [P]

2026-07-12 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, short

Summary

A user encountered irregular validation and training loss learning curves and an R2 score of 1.00 when applying Hyperband automatic tuning to an Artificial Neural Network (ANN) model for price prediction. The model's architecture was optimized using `keras_tuner.Hyperband` with `max_epochs=50` and `factor=3`, exploring `relu` or `tanh` activations, layer units up to 500, and learning rates of 0.01, 0.001, or 0.0001. Despite prior Standard Scaling of the target price data, which ranged from £100 to £170,000, the resulting learning curves exhibited significant volatility, prompting concerns about potential code errors or a fundamental misunderstanding of the model's behavior. Initial feedback from other users points to possible issues with data normalization or the overall model setup, suggesting the high R2 score might indicate severe overfitting.

Key takeaway

For Machine Learning Engineers debugging erratic model performance after hyperparameter tuning, you should meticulously review your data normalization steps and the overall model setup. An R2 score of 1.00 combined with highly irregular learning curves strongly indicates overfitting or data leakage, even with tools like Hyperband. Verify that your target variable, especially price data, is correctly scaled and that validation splits maintain data integrity across epochs. This ensures your tuning efforts yield meaningful and generalizable results, preventing misleading performance metrics.

Key insights

Hyperparameter tuning can yield erratic learning curves and overfitting if data preprocessing or model setup is flawed.

Principles

• An R2 score of 1.00 often signals overfitting.
• Irregular validation loss suggests data issues.
• Hyperparameter tuning requires careful setup.

Method

The described method involves using `keras_tuner.Hyperband` to optimize an ANN's `Dense` layer units (1-500), activation functions (`relu`, `tanh`), and Adam optimizer learning rates (1e-2, 1e-3, 1e-4) with `EarlyStopping`.

In practice

• Normalize target data for stable training.
• Review learning curves for data consistency.
• Verify preprocessing steps thoroughly.

Topics

• Hyperparameter Tuning
• Artificial Neural Networks
• Keras Tuner
• Learning Curve Analysis
• Overfitting Diagnostics
• Data Preprocessing

Best for: AI Scientist, Research Scientist, Machine Learning Engineer, AI Student, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.