Frequently Asked Questions
I have extensive experience with classification algorithms such as Logistic Regression, Support Vector Machines (SVMs), Random Forests, Gradient Boosting (XGBoost, LightGBM), and basic Neural Networks. For predictive modeling, I frequently use Linear Regression, Ridge/Lasso, and Time Series models. My focus is on selecting the most appropriate algorithm for the problem and data.
Yes, I have experience handling diverse data types common in engineering (time-series sensor data, logs, operational parameters) and healthcare (EHR data, claims data, vital signs). I am proficient in data cleaning, transformation, and feature engineering tailored to these domains.
Model interpretability is crucial, especially in domains like healthcare and engineering where understanding predictions is vital. I use techniques such as SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations), permutation importance, and partial dependence plots to explain model predictions and understand feature importance.
I have practical experience with major cloud platforms including AWS (S3, EC2, SageMaker, Lambda), GCP (Cloud Storage, Compute Engine, AI Platform), and Azure (Blob Storage, Virtual Machines, Azure ML). I am comfortable building and deploying ML solutions within these environments.
The choice of evaluation metric depends heavily on the problem. Some common metrics for classification models include:
Other important metrics include Specificity, F-beta Score, and Log Loss.
| Metric | Description | When to Use |
|---|---|---|
| Accuracy | Overall proportion of correct predictions. | Good for balanced datasets. |
| Precision | Proportion of positive identifications that were actually correct. | When minimizing False Positives is critical. |
| Recall (Sensitivity) | Proportion of actual positives that were identified correctly. | When minimizing False Negatives is critical. |
| F1-Score | Harmonic mean of Precision and Recall. | Good for imbalanced datasets, balances Precision and Recall. |
| AUC-ROC | Area under the Receiver Operating Characteristic curve; measures ability to distinguish classes. | For evaluating model performance across various thresholds. |