Understanding Datasets, Features, and Labels

What Is a Dataset?

A dataset is a collection of observations used to analyze a problem or train a machine learning model.

Datasets are often organized into rows and columns:

• Each row represents an observation or example.
• Each column represents a feature or target.

For example, a dataset for predicting student performance might include study hours, attendance, and previous test scores.

What Are Features?

Features are the input variables a model uses to learn patterns or make predictions.

For a house-price prediction model, possible features include:

• Area of the property.
• Number of bedrooms.
• Property age.
• Location.
• Distance from public transportation.

A useful feature should provide relevant information about the target. Irrelevant, unreliable, or misleading features can reduce model quality.

What Is a Label?

A label is the target value that a supervised learning model attempts to predict.

For example, in a dataset used to predict whether an email is spam:

• Email text is an input.
• Message characteristics can be features.
• The label indicates whether the email is spam or legitimate.

In a regression problem, the label is a numerical value. In a classification problem, it is typically a category.

Unsupervised learning generally does not require a predefined target label.

Preparing Data for Machine Learning

Raw data often needs preparation before it can be used effectively.

Common preparation tasks include:

• Removing duplicate records.
• Handling missing values.
• Correcting inconsistent formats.
• Converting categorical values into numerical representations when required.
• Scaling numerical features when the algorithm benefits from it.
• Checking for incorrect values and potential data leakage.

For example, a dataset may represent age as numbers but record monthly income using text symbols. These fields may need cleaning and conversion before model training.

Training, Validation, and Test Data

A dataset is often separated into subsets for different purposes.

Training set: Used to fit the model.

Validation set: Used to compare model configurations, tune hyperparameters, or choose a decision threshold.

Test set: Used for a final evaluation on data that has not been used for model fitting or tuning.

A simple workflow might use 70% of the data for training, 15% for validation, and 15% for testing. These percentages are examples, not universal requirements. Smaller datasets may need cross-validation or other strategies.

The test set should remain separate until the final evaluation. Repeatedly using it to make development decisions can produce an overly optimistic estimate of real-world performance.

Understanding Data Leakage

Data leakage occurs when information that would not legitimately be available at prediction time influences model training or evaluation.

For example, imagine predicting whether a customer will cancel a subscription using a feature that records whether the account was already closed. This feature reveals information about the outcome and can make the model appear more accurate than it really is.

Data leakage can lead to poor performance after deployment even when evaluation results initially look excellent.