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What’s Wrong With My Model? Diagnosing Issues and Finding Solutions

A Handy Reference Cheat-Sheet

If your model is not performing at the level you believe it should be performing, you’ve come to the right place! This reference article will detail common issues and solutions in model building.

Neural Networks

Problem: Your neural network performs very well in training but poorly on validation (testing) sets.
Issue: Your neural network is probably overfitting to the data. This means that instead of actually searching for deeper connections within the training examples, it is ‘taking the easy way out’ and simply memorizing all of the data points, which is possible given its large architecture.
Solutions:

• Simplify the model architecture. When your neural network has too many layers and nodes, it may give the model the opportunity to memorize the data instead of actually learning generalized patterns. By reducing the storage capacity of a neural network, you are taking away its ability to ‘cheat’ its way to high performance.
• Early stopping is a form of regularization and involves stopping a neural network from training right where the testing error is the smallest.
• Data augmentation, which…