Model Training
Underfitting
When an AI model is too simple to capture the patterns in the data, like trying to draw a curve with a straight line.
Definition
A modeling error where a model is too simple to capture the underlying patterns in the training data, resulting in poor performance on both training and test data.
Why it matters
The opposite of overfitting, often solved by using more complex models, more features, or more training time.
Related terms in Model Training
Adversarial Training
Teaching an AI to defend itself by constantly attacking it with tricky or malicious inputs during training.
Autoencoders
A neural network that learns to compress data into a small code and then unzip it back to the original.
Distillation (Model Distillation)
Teaching a small, fast AI model to mimic a large, expensive one, so you get similar results at a fraction of the cost.
Dropout
Randomly turning off some neurons during training so the AI doesn't over-memorize and can generalize better.
From vocabulary to outcomes
Ready to put Underfitting to work?
Knowing the term is step one. Deploying it inside a revenue architecture that compounds is what Sophizo builds.
Book a Discovery Call