Feature importance

How it is used

Used when analyzing trained models, comparing inputs, or explaining results to stakeholders. Different methods can produce different importance scores.

Example sentences

1. The analyst reviewed feature importance to explain why the churn model relied heavily on support ticket volume.

   Here the term refers to evidence about which input variable had stronger influence on the model's output.

2. Before presenting to the client, we compared feature importance across two models trained on the same retail dataset.

   In this example, it is used as a comparison tool to see whether both models depend on similar signals.

Common collocations

• feature importance score
• global feature importance
• compare feature importance

Related terms

• machine learningA branch of artificial intelligence in which computers learn patterns from data and use them to make predictions or decisions, instead of following rules written by hand.
• modelThe result of training: a program with learned parameters that takes an input, such as an image or a sentence, and produces an output, such as a label, a number or a text.

More terms: Machine Learning and Data Processing

• artificial intelligence
• dataset
• training data
• label
• train
• supervised learning
• neural network
• deep learning
• loss function
• overfitting
• test set
• accuracy