To determine features and labels in a dataset, start by identifying the goal of your machine learning task. Features are the input variables used to predict an outcome, while labels (or targets) are the output variables you want to predict. For example, in a dataset predicting house prices, features might include square footage, number of bedrooms, and location, while the label would be the house price itself. The key distinction is that features describe characteristics of the data, and labels represent the value you’re trying to learn or predict.
When working with structured data, features are typically columns in a table. To identify them, ask: “Which columns describe attributes that could influence the outcome?” Labels, on the other hand, are often a single column explicitly labeled as the target. For instance, in a medical dataset predicting diabetes risk, features might include age, blood sugar levels, and BMI, while the label would be a binary indicator (1 or 0) for diabetes diagnosis. If your dataset lacks a clear target column, you may need to define the problem more precisely—e.g., deciding whether to predict a category (classification) or a numerical value (regression).
In practice, preprocessing steps like removing irrelevant columns or handling missing values can clarify which data points are features. For example, a customer churn dataset might include columns like “customer ID” or “transaction timestamp” that don’t directly impact churn—these should be excluded as features. Tools like pandas in Python can help isolate features (X = df.drop('label_column', axis=1)) and labels (y = df['label_column']). Always validate your choices by testing whether the features logically relate to the label and whether excluding certain data improves model performance.