Feature (Machine Learning)

In machine learning, a feature is an input variable the model uses to make a prediction: one measurable property of each example, like a column in a spreadsheet. For a page that could be word count, number of internal links or average position; for a customer, how recently and how often they bought. This is a different thing from SERP features (featured snippets, People Also Ask and other search result elements), even though both words appear in SEO data work.

How it works

Each example in your training data is described by a set of features, sometimes called a feature vector. In supervised learning, the model learns how those features relate to the label, the answer it should predict. A regression model predicting clicks might use impressions and position as features; a classifier spotting AI-generated queries might use reading grade level and entity density.

Features come in a few types: numeric (sessions, word count), categorical (page type, country), boolean (has schema or not) and text. Text has to be turned into numbers before most models can use it, which is what feature extraction does, through counts, TF-IDF or embeddings.

Feature engineering is the work of choosing, creating and preparing features: deriving average order value from orders and revenue, encoding categories as numbers, and scaling values so that a feature measured in thousands does not drown out one measured in single digits. When there are too many features, dimensionality reduction compresses them.

How it’s used in SEO and marketing

• Building customer segments from RFM features (recency, frequency, monetary value) plus engineered ones such as purchase variety.
• Classifying queries or pages using features like length, readability, entity counts or stop-word ratio.
• Forecasting traffic with features such as seasonality, past clicks and ranking position.
• Reading feature importance to see which signals drive a model’s decisions, a practical step towards explainability.

Example

In the Semantic Keyword Research course practical on telling synthetic queries from user-typed ones, each query is described by features such as reading grade level, entity density and stop-word ratio. A model is trained on labelled examples, and feature importance analysis shows which of those signals separate the two groups most.

Limitations and things to watch

• Garbage in, garbage out: missing, inconsistent or leaky features (ones that secretly contain the answer) produce models that look great in testing and fail in use.
• More features is not always better. Irrelevant ones add noise and raise the risk of overfitting.
• Unscaled features can dominate distance-based methods such as K-Means clustering.
• Feature importance shows what the model relies on, not what causes the outcome.
• Check whether “feature” means an ML input, a SERP feature or a product feature before reading a tutorial.

Learn it on MLforSEO

• Customer segmentation with machine learning using three clustering approaches includes feature engineering and scaling.
• Synthetic vs user query classification notebook shows feature importance in action.
• The Characteristics of a Machine Learning Problem covers the data side of ML projects.
• Introduction to Machine Learning for SEO and Semantic ML-enabled Keyword Research courses.