Explain PCA and L2 Normalization in Machine Learning
Context
You have developed a binary classifier. Your preprocessing pipeline involved applying Principal Component Analysis (PCA) and L2 normalization before model training. For evaluation, you utilized ROC-type metrics and considered logistic regression as a baseline, alongside other alternative models.
Questions
- Preprocessing
- Explain your rationale for applying PCA and L2 normalization prior to training. What was the specific order of these operations? How do these techniques relate to variance reduction and addressing multicollinearity?
- Modeling Choices
- Discuss logistic regression as your chosen baseline model. Compare it to other reasonable alternative models you considered, detailing their respective pros, cons, and the scenarios where each would be most effective.
- Backpropagation
- Provide a high-level description of backpropagation as implemented in modern neural networks. Include an explanation of how loss gradients are computed and subsequently propagated through the network.
- Knowledge-informed ML and Thresholding
- Define knowledge-informed (or knowledge-guided) machine learning. Provide concrete examples of how you incorporated domain knowledge into your machine learning pipeline.
- Describe your process for tuning the decision threshold. Under what specific conditions can (or cannot) both the False Positive Rate (FPR) and True Positive Rate (TPR) simultaneously improve? Detail your methodology and the trade-offs involved.
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Core dilemma
The central engineering conflict is holistically optimizing a machine learning pipeline for a specific business objective, navigating the inherent trade-offs between model complexity, interpretability, performance metrics (especially under imbalanced classes or specific cost functions), and the practical application of domain knowledge across the entire ML lifecycle.
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Explain PCA and L2 Normalization in Machine Learning
Context
You have developed a binary classifier. Your preprocessing pipeline involved applying Principal Component Analysis (PCA) and L2 normalization before model training. For evaluation, you utilized ROC-type metrics and considered logistic regression as a baseline, alongside other alternative models.
Questions
- Preprocessing
- Explain your rationale for applying PCA and L2 normalization prior to training. What was the specific order of these operations? How do these techniques relate to variance reduction and addressing multicollinearity?
- Modeling Choices
- Discuss logistic regression as your chosen baseline model. Compare it to other reasonable alternative models you considered, detailing their respective pros, cons, and the scenarios where each would be most effective.
- Backpropagation
- Provide a high-level description of backpropagation as implemented in modern neural networks. Include an explanation of how loss gradients are computed and subsequently propagated through the network.
- Knowledge-informed ML and Thresholding
- Define knowledge-informed (or knowledge-guided) machine learning. Provide concrete examples of how you incorporated domain knowledge into your machine learning pipeline.
- Describe your process for tuning the decision threshold. Under what specific conditions can (or cannot) both the False Positive Rate (FPR) and True Positive Rate (TPR) simultaneously improve? Detail your methodology and the trade-offs involved.