MACHINE LEARNING

Feature Engineering

Machine Learning

Definition

Feature engineering is the process of creating, selecting, and transforming the variables (features) that feed an ML model. For example, from an order date you can derive features like day of the week, month, distance to the nearest holiday. It is often the factor that makes the difference between a mediocre and an excellent model in business applications.

Related terms

AI Fundamentals

Dataset

A dataset is a structured collection of data used to train, validate, or test an AI model. Dataset quality is directly proportional to the quality of results: incomplete, imbalanced, or erroneous data produce unreliable models. For SMEs, organizing and cleaning their business data is often the first step toward AI adoption.

Machine Learning

Random Forest

Random Forest is an ML algorithm that combines hundreds of decision trees to produce robust and accurate predictions. Each tree votes and the final result is the majority's choice. It is popular in business because it works well with minimal tuning, handles missing data, and provides information on which variables are most important for the prediction.

Machine Learning

Gradient Boosting

Gradient boosting is an ML technique that builds models sequentially, where each new model corrects the errors of the previous one. XGBoost, LightGBM, and CatBoost are popular implementations. It is often the best-performing algorithm for structured tabular data (such as spreadsheets and business databases), making it ideal for financial and operational predictions.

EXPLORE

More terms in Machine Learning

Supervised Learning

Supervised learning is the ML technique in which the model learns from labeled examples: for each input, the correct output is provided. For example, by showing thousands of emails labeled as 'spam' or 'not spam', the model learns to classify new emails. It is the most common approach in business applications because it produces reliable and interpretable results.

Unsupervised Learning

Unsupervised learning is the ML technique in which the model finds patterns in data without predefined labels. The model autonomously discovers hidden structures such as groups of similar customers (clustering) or data anomalies. It is useful when you don't know what to look for: AI can reveal market segments or anomalous behaviors you hadn't considered.

Reinforcement Learning

Reinforcement learning is an ML technique in which an agent learns by interacting with an environment and receiving rewards or penalties. It is used to optimize complex strategies such as dynamic pricing, logistics routing, and warehouse management. The agent experiments with different actions and learns which strategy maximizes long-term results.

Classification

Classification is the ML task that assigns a categorical label to an input. Examples: classifying emails (spam/not spam), support tickets (urgent/normal/low), documents (invoice/order/contract), or product images (compliant/defective). It is one of the most widespread and immediate uses of AI in SMEs, with fast and measurable ROI.

Regression

Regression is the ML task that predicts a continuous numerical value. Unlike classification which assigns categories, regression estimates quantities: sales forecasting, real estate price estimation, production cost forecasting. It is fundamental for demand forecasting and financial planning in SMEs.

Clustering

Clustering is an unsupervised ML technique that automatically groups similar data together. It is used to segment customers based on purchasing behavior, group similar products, identify patterns in support calls. The model doesn't need to be told how many groups to find (in many algorithms): it discovers them autonomously in the data.

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