Problem Statement: A company seeks to refine a spam detection neural network using the Spambase dataset (4,601 emails, 57 features) with comprehensive evaluation metrics.
Approach: I selected the best-tuned model, computed predictions, evaluated accuracy, F1, precision, recall, and analysed performance using a confusion matrix.
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 normalise features to mean=0 and standard deviation=1. This ensures consistent scaling across features for optimal neural network training.
The standardisation 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 explore comprehensive evaluation metrics for spam detection.
Load TensorFlow, Keras, scikit-learn, numpy, pandas, and the Spambase dataset.
Split into training and test sets, apply standardisation to normalise features.
Choose the highest-performing model architecture from previous grid search experiments.
Train the selected model architecture with early stopping and save to disk for reuse.
Generate predictions on test data and calculate comprehensive evaluation metrics.
Interpret metrics and confusion matrix to understand model strengths and weaknesses.
Conclude with recommendations based on performance analysis.
Selected the highest-performing model from previous grid search experiments with the following characteristics:
Implemented early stopping to prevent overfitting and saved the best model to enable reuse and deployment.
1. Initialise model with 64-32-16x4-1 architecture
2. Compile with binary cross-entropy loss and accuracy metric
3. Configure early stopping and model checkpoint callbacks
4. Train with batch_size=16 for up to 100 epochs (early stopping typically activates around epoch 14)
5. Save best model as 'best_model.h5'
Applied the trained model to the test set to generate probability predictions, which were then converted to binary classifications using a threshold of 0.5.
Computed comprehensive metrics over 10 runs to ensure statistical robustness of the evaluation results.
Proportion of correctly classified emails (both spam and non-spam)
Of all emails classified as spam, what proportion were actually spam
Of all actual spam emails, what proportion were correctly identified
Harmonic mean of precision and recall, balancing both concerns
The confusion matrix reveals the distribution of predictions across classes:
The low false positive rate indicates the model is conservative when flagging emails as spam, which is important to avoid filtering out legitimate emails.
High Precision (0.9416): When the model identifies an email as spam, it's right about 94% of the time. This minimises the risk of important emails being incorrectly filtered.
Moderate Recall (0.8787): The model correctly identifies about 88% of all actual spam emails. Some spam may still reach the inbox.
Strong F1 Score (0.9090): Indicates a good balance between precision and recall, though slightly favouring precision over recall.
Solid Accuracy (0.9254): Overall, the model correctly classifies 92.5% of all emails.
The selected model (64-32-16x4-1 architecture, trained for 14 epochs with batch size 16) performs well on spam detection, achieving an accuracy of 0.9254 and F1 score of 0.9090. The high precision (0.9416) ensures minimal loss of legitimate emails, although the moderate recall (0.8787) indicates some spam may still reach the inbox.
The model excels at conservative spam detection, prioritising the preservation of legitimate emails over catching every spam message. This approach aligns well with business needs where missing important communications would be more costly than receiving occasional spam. Further tuning could enhance recall for more comprehensive filtering whilst maintaining the model's strong precision.