Machine Learning
The Hidden Cost of Training Data Quality
Bad labels and inconsistent data cost more than bad models do, because they're harder to detect and they cap your ceiling no matter how good the model gets.
3 min read
Tag
Data Science
14 articles
Machine Learning
Hyperparameter Tuning Strategies That Don't Waste Compute
Grid search doesn't scale and random search leaves gains on the table — a practical comparison of tuning strategies and when each one earns its cost.
3 min read
Machine Learning
Time-Series Forecasting: Classical Methods vs. Neural Approaches
When ARIMA and exponential smoothing still beat a neural forecaster, and when the added complexity of a deep learning model actually pays off.
3 min read
Machine Learning
Recommendation Systems: Collaborative vs. Content-Based Filtering
The two foundational approaches to recommendations, their failure modes, and why most production systems end up combining both rather than picking one.
3 min read
Machine Learning
A/B Testing Machine Learning Models in Production
Offline metrics tell you a new model is better in theory. A/B testing tells you whether it actually moves the numbers that matter — here's how to do it right.
3 min read
Machine Learning
Choosing Evaluation Metrics: Precision, Recall, ROC, and Calibration
A practitioner's guide to picking the right evaluation metric for a classification problem, and why a well-ranked model can still be badly calibrated.
3 min read
Machine Learning
Class Imbalance: Sampling, Weights, and the Right Metrics
Practical techniques for imbalanced classification, and why accuracy is the wrong number to optimize when the positive class is 2% of your data.
3 min read
Machine Learning
Train/Validation/Test Splits Done Right (Including Time Series)
Random splits are wrong more often than practitioners assume — a walkthrough of when to use them and when time-based or grouped splits are required.
3 min read
Machine Learning
Overfitting and Regularization in Practice
A practical guide to spotting overfitting early and choosing the right regularization technique for the model family you're actually using.
3 min read
Machine Learning
Data Leakage: The Silent Model Killer
Data leakage produces models that look excellent in validation and fail in production. Here's how it sneaks in and the checks that catch it early.
3 min read
Machine Learning
Feature Engineering Patterns That Actually Generalize
A field guide to feature engineering techniques that hold up outside the notebook, from ratios and windowed aggregates to target encoding done safely.
3 min read
Machine Learning
Why XGBoost and LightGBM Still Win on Tabular Data
Deep learning dominates images and text, but gradient boosted trees remain the default choice for tabular data — here's the technical reason why.
3 min read
Machine Learning
Detecting and Handling Model Drift in Production
Why models degrade quietly after deployment, how to detect drift with statistical tests before it shows up in business metrics, and what to do about it.
3 min read
Machine Learning
MLOps Pipelines: From Notebook to Production
How to turn a working Jupyter notebook into a reproducible, monitored production pipeline without rewriting everything from scratch.
3 min read