Problem Statement: A company aims to enhance a spam detection neural network using the Spambase dataset (4,601 emails, 57 features) via hyperparameter tuning.
Approach: I extended the base model with additional layers, tuned batch sizes and epochs using grid search, and analyzed performance to optimize accuracy.
Loaded 4,601 emails with 57 features and binary spam/non-spam labels. Split the dataset into 80% training (with 10% used for validation) and 20% test sets to ensure proper model evaluation.
Applied StandardScaler to normalize features to mean=0 and standard deviation=1. This ensures consistent scaling across features for optimal neural network training.
The standardization was applied only to the training data, and then the same transformation was applied to test data to prevent data leakage.
Begin the project to optimize neural network performance through hyperparameter tuning.
Load TensorFlow, Keras, scikit-learn, numpy, pandas, and the Spambase dataset.
Split into training and test sets, apply standardization to normalize features.
Extend the base neural network architecture with additional hidden layers.
Test different batch sizes to find optimal training efficiency and performance.
Determine the optimal number of training iterations to balance fit and overfitting.
Analyze the results to identify the best hyperparameter configuration.
Conclude with optimized hyperparameters and recommendations for implementation.
Extended the base neural network model by adding multiple hidden layers to increase model complexity and capture more intricate patterns in the data.
Configured the neural network with appropriate loss function, optimizer, and metrics for binary classification.
The progressively narrowing structure (64→32→16→16→16→16→1) allows the network to:
Tested batch sizes [16, 32, 64] with a fixed number of 10 epochs to determine the optimal training efficiency.
Finding: Smaller batch sizes (16) produced slightly better accuracy, likely due to more frequent weight updates.
Tested different numbers of epochs [10, 20, 30] with a fixed batch size of 64 to determine optimal training duration.
Finding: Performance peaked around 14-15 epochs, with diminishing returns and potential overfitting beyond that point.
Conducted comprehensive grid search across 100 model runs to systematically identify optimal hyperparameter combinations.
Selected Configuration: 14 epochs, batch size of 16
The best configuration (14 epochs, batch size=16) achieved superior performance on the spam detection task.
Several factors contributed to the performance improvements observed through hyperparameter tuning:
Successfully enhanced the Spambase neural network by adding four hidden layers (64-32-16-16-16-16-1) and conducting systematic hyperparameter tuning. The grid search across 100 runs identified optimal hyperparameters (14 epochs, batch size=16), achieving an accuracy of ~0.95, effectively balancing model fit and generalization capabilities.
While the current model achieves excellent performance, future enhancements could explore regularization techniques like dropout, learning rate scheduling, or advanced architectures like attention mechanisms to further boost spam detection capabilities.