Tabular Data Tasks

Tabular data is structured information organized in rows and columns, where each row represents an observation and each column represents a feature. This is the most common data format in business, science, and analytics—think spreadsheets, databases, and CSV files.

Supervised Learning Tasks

Classification

Predict which category an observation belongs to based on its features. The target variable is discrete.

Examples: Is this email spam? Will this customer churn? Which product category does this belong to?

Common models: Logistic Regression, Random Forest, K-Nearest Neighbors

Regression

Predict a continuous numerical value based on input features. The target variable is a real number.

Examples: What will the house price be? How much will sales be next month? What's the expected temperature?

Common models: Linear Regression, Polynomial Regression

Unsupervised Learning Tasks

Clustering

Group similar observations together without predefined labels. Discovers natural structure in data.

Examples: Customer segmentation, anomaly detection, organizing documents

Common methods: K-Means, hierarchical clustering, DBSCAN

Dimensionality Reduction

Reduce the number of features while preserving essential information. Makes data easier to visualize and process.

Examples: Compress high-dimensional data, visualize in 2D/3D, remove redundant features

Common methods: PCA, t-SNE, UMAP

Sequential Data Tasks

Time Series Forecasting

Predict future values based on past observations ordered in time. Unlike standard tabular tasks, the sequence matters.

Examples: Stock price prediction, demand forecasting, weather prediction

Common methods: ARIMA, Prophet, exponential smoothing, LSTMs

Model Families for Tabular Data

Different algorithms share core principles and can be adapted to multiple tasks. See Model Families for details on:

Decision Trees: Recursive splitting based on feature values. Interpretable, handles mixed data types, prone to overfitting.

Linear Models: Linear combinations of features. Fast, interpretable, assumes linear relationships. Includes linear regression, logistic regression, Ridge, Lasso.

Support Vector Machines: Maximize margin between classes or fit within error tubes. Handles high dimensions well with kernels. Includes SVC, SVR.

Tree Ensembles: Combine multiple decision trees. More robust than single trees. Includes Random Forest, Gradient Boosting, XGBoost.

Recommendation Systems: Predict user preferences and rank items. Collaborative filtering, content-based, matrix factorization.

Key Characteristics of Tabular Data

Structured format: Clear rows (observations) and columns (features). Each cell has a specific meaning.

Mixed feature types: Can include continuous numbers, discrete categories, ordinal values, dates, and text.

Feature engineering: Often requires creating new features, handling missing values, encoding categories, and scaling.

Interpretability: Many tabular models (linear models, decision trees) are interpretable, which is valuable in business and scientific applications.

Small to medium datasets: Unlike images or text, tabular datasets are often smaller (thousands to millions of rows rather than billions). This makes tree-based models and classical ML very competitive with neural networks.

Practical Workflow

Explore the data: Understand distributions, missing values, correlations, outliers
Clean and preprocess: Handle missing values, encode categories, scale features
Feature engineering: Create new features, transform existing ones, select relevant features
Choose appropriate task: Classification, regression, clustering, etc.
Select models: Start simple (linear models, decision trees), then try ensembles
Evaluate: Use appropriate metrics, cross-validation, check for overfitting
Interpret: Understand which features matter, validate predictions make sense