Machine LearningData ScienceAI StrategyGuide

Picking the Right ML Model (Without Overthinking It)

January 28, 20267 min readCoraLabs Team

The Most Common Mistake

Here's what happens: someone reads about deep learning, gets excited, and tries to throw a neural network at a problem that a decision tree could solve in 20 minutes. Don't be that person.

The right model depends on what you're predicting, what data you have, and what you'll do with the answer. That's it.

Step 1: What Kind of Problem Is This?

Classification - Is this email spam? Will this customer leave? Is this transaction suspicious?

Regression - What will revenue be next month? How much energy will this building use?

Clustering - What customer groups exist? Which products are similar?

Time Series - What will demand look like in Q3? When will this sensor exceed threshold?

Recommendation - What should we suggest to this user?

NLP - Summarize this. Extract names and dates. Tell me if this review is positive.

If you can't clearly state which of these your problem is, stop and figure that out first.

Step 2: Look at Your Data

What You HaveWhat It Means

**Small dataset**Simpler models. Trees, linear models. Deep learning will overfit.

**Noisy data**Use something robust: Random Forest, XGBoost. They handle mess well.

**Tabular data**Tree-based models. Seriously, just use XGBoost.

**Images**CNNs, pre-trained models with transfer learning.

**Text**Transformers. Fine-tune BERT or use an LLM.

**Time series**Prophet, ARIMA, or LSTMs depending on complexity.

**No labels**Unsupervised: clustering, dimensionality reduction.

Step 3: Match Model to Problem

Structured/tabular data (most business problems):

XGBoost or LightGBM - start here. They win Kaggle competitions for good reason

Logistic Regression - great baseline for classification. Fast, interpretable

Random Forest - solid all-rounder, hard to mess up

Text/NLP:

Fine-tuned transformers (BERT family) for specific tasks

Embeddings + search for semantic matching

LLMs with good prompts for flexible, general-purpose work

Time Series:

Prophet for straightforward forecasting (it's surprisingly good)

LSTM / Temporal Fusion Transformer for complex multi-variate series

XGBoost with lag features if you want to stay in tabular-land (also surprisingly good)

Images:

Pre-trained CNNs (ResNet, EfficientNet) with transfer learning

YOLO for real-time object detection

Vision Transformers when accuracy matters more than speed

The Honest Truth

80% of business ML problems are solved well by three things:

XGBoost for structured data

Fine-tuned transformers for text

Pre-trained CNNs for images

The other 20% (real-time systems, multi-modal, reinforcement learning) need specialized expertise and custom work. But most companies aren't there yet, and that's fine.

Mistakes We See All the Time

Using a cannon to kill a fly - deep learning for 500 rows of data

Skipping the baseline - always compare against a simple model first. You'd be surprised

Data leakage - accidentally including future information in training data. Instant fake accuracy

Bad validation - using random splits on time-series data (don't do this)

Ignoring features - a creative feature is often worth more than a fancier model

When to Call for Help

Consider bringing in experts when:

You have data but nobody who knows ML

Your first models aren't good enough and you're not sure why

You need to go from "works in a notebook" to "works in production"

The problem is domain-specific and you need specialized knowledge

We help businesses figure this out at CoraLabs. Problem framing, model selection, development, and getting it live. Grab a free consultation if you want to talk it through.

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