MACHINE LEARNING

F1 Score

Machine Learning

Definition

The F1 score is the harmonic mean of precision and recall, providing a single metric that balances both. A high F1 score indicates the model is both precise in its positive predictions and capable of finding most positive cases. It is the most commonly used metric for evaluating classification models in business contexts where both precision and completeness are needed.

Related terms

Machine Learning

Precision

Precision is a metric that measures how many of a model's positive predictions are actually correct. If an anomaly detection system flags 100 suspicious transactions and 80 are truly fraudulent, the precision is 80%. It is important when false positives have a high cost, such as in fraud detection or diagnostics.

Machine Learning

Recall

Recall measures how many of the actual positive instances are correctly identified by the model. If there are 100 phishing emails and the system detects 90, the recall is 90%. It is critical when the cost of missing a positive case is high, such as in security, quality control, and medical diagnostics.

Machine Learning

Confusion Matrix

A confusion matrix is a table that visualizes the performance of a classification model, showing true positives, true negatives, false positives, and false negatives. It helps understand not just how much the model is wrong, but how it is wrong. It is fundamental for deciding whether an AI model is ready for production and where it needs improvement.

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.

See all glossary terms →

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