Optimizing and Evaluating the Final Neural Network Model
Explore how to optimize and evaluate Multi-Layer Perceptron models by comparing performance metrics, addressing overfitting risks, and selecting appropriate activation functions. Learn key strategies for data preprocessing, class weighting, and parameter tuning relevant to rare event prediction with imbalanced data.
The final model selection is a critical step, relying not just on performance metrics but also on the model’s ability to generalize effectively to unseen data. Our comparative analysis of models, as summarized in the table, reveals the nuanced trade-offs between model complexity, performance metrics, and the risk of overfitting. The selection is made by comparing the validation results.
MLP Models Comparison
Model | Validation | |||
Loss | f1-score | Recall | fpr | |
Baseline | Increasing | 0.13 | 0.08 | 0.001 |
Dropout | Non-increasing | 0.00 | 0.00 | 0.000 |
Class weights | Non-increasing | 0.12 | 0.31 | 0.102 |
| Nonincreasing | 0.04 | 0.02 | 0.001 |
| Non-increasing | 0.12 | 0.08 | 0.001 |
The baseline model has higher ...