I've watched brilliant engineers spend weeks building complex neural networks to solve problems that logistic regression could handle in minutes. I've also seen the reverse—people using simple models on problems that absolutely needed deep learning. The common thread: they didn't have a framework for model selection.
The Hierarchy of Models
Here's my mental model for thinking about AI problems:
Level 1: Simple Baselines
Linear Regression, Logistic Regression, Decision Trees, Naive Bayes
Level 2: Ensemble Methods
Random Forest, Gradient Boosting (XGBoost, LightGBM, CatBoost)
Level 3: Deep Learning
Neural Networks, CNNs, RNNs, Transformers
When to Use What
Start here if:
- Your data is tabular (rows and columns)
- You have less than 100K samples
- Interpretability matters
- Training speed is important
Use simple models (Linear, Logistic, Trees):
- Clear linear relationships in data
- Need to explain predictions
- Limited data available
- Fast iteration needed
Use ensembles (Random Forest, XGBoost):
- Tabular data with complex patterns
- Need good accuracy quickly
- Moderate data size
- Handle mixed feature types
Use deep learning:
- Images, text, audio, video
- Very large datasets (>100K samples)
- Need state-of-the-art accuracy
- Complex patterns, no obvious features
My Selection Framework
Ask yourself these questions in order:
- What type of data? (Tabular → trees; Images → CNN; Text → RNN/Transformer)
- How much data? (Little → simpler models; Lots → can try deep learning)
- Interpretability needed? (Yes → linear/trees; No → anything goes)
- Time constraints? (Fast → sklearn; Slower → can try more complex)
The Baseline Rule
Before trying anything fancy, always establish a baseline with a simple model. If your complex neural network doesn't beat a well-tuned logistic regression by a meaningful margin, something is wrong—with your data, your features, or your understanding of the problem.
Start simple. Add complexity only when justified. That's the pragmatic path to working AI systems.