The Gap Between Theory and Production
Machine learning courses teach dozens of algorithms. Production systems use a much smaller set of them, chosen for different reasons than academic performance benchmarks suggest. This guide covers what actually shows up in real ML deployments, when each is the right choice, and what tends to go wrong when teams reach for the wrong tool.
Linear and Logistic Regression — Underrated
These are the algorithms most teams abandon too quickly in pursuit of something more sophisticated. Linear regression for continuous predictions, logistic regression for binary classification — both produce interpretable outputs, train fast, and generalize well when the underlying relationships are roughly linear. If your problem fits the assumptions and you can explain the model to a stakeholder, the simpler algorithm is almost always preferable. Complexity should be earned by demonstrated performance gaps, not chosen by default.
Decision Trees and Gradient Boosting
Decision trees are intuitive but overfit easily. Gradient boosted trees — XGBoost, LightGBM, CatBoost — are among the most consistently useful algorithms in production ML. They handle mixed data types well, require relatively little preprocessing, and regularly outperform neural networks on tabular data. If you're doing classification or regression on structured data and you're not already using a boosting approach, it's worth benchmarking.
Neural Networks — When They're Worth It
Neural networks genuinely excel in a specific set of domains: image recognition, natural language processing, time-series with complex patterns, and tasks where the feature engineering required for traditional ML would be prohibitively complex. They're not the right default for most business ML problems. They're harder to debug, require significantly more data to generalize well, and the computational cost is real. Reach for them when the problem genuinely needs them — not because they're impressive.
Model Selection in Practice
The most important variable in model selection isn't the algorithm — it's the quality of the training data. A well-prepared dataset with thoughtful feature engineering will outperform a sophisticated model trained on messy data almost every time. The workflow that produces reliable production models: understand the problem clearly, clean and validate the data obsessively, establish a baseline with a simple model, and then improve from there with justification. Don't start with the most complex option available.
"The best ML model is the simplest one that solves the problem reliably enough to deploy. Complexity should always earn its place."
Yinfocore builds ML systems that are designed to work in production — not just in notebooks. If you're trying to take a model from experiment to deployment, or if your current model performance is disappointing, let's look at it together.