What is Feature Importance?
Features in machine learning, also known as variables or attributes, are individual measurable properties or characteristics of the phenomena being observed. They serve as the input to the model, and their quality and quantity can greatly influence the accuracy and efficiency of the model. There are three primary categories of features:
- Numerical Features: These features represent quantitative data, expressed as numerical values (integers or decimals). Examples include temperature (°C), weight (kg), and age (years).
- Categorical Features: These features represent qualitative data, signifying the category to which a data point belongs. Examples include hair color (blonde, brunette, black) and customer satisfaction (satisfied, neutral, dissatisfied).
- Ordinal Features: These features are a subtype of categorical features, possessing an inherent order or ranking. Examples include movie ratings (1 star, 2 stars, etc.) and customer service experience (poor, average, excellent).
Feature Importance with Random Forests
Features in machine learning, plays a significant role in model accuracy. Exploring feature importance in Random Forests enhances model performance and efficiency.