MACHINE LEARNING

Cross-Validation

Machine Learning

Definition

Cross-validation is a technique for evaluating the real performance of an ML model by dividing data into parts (folds) and using each in turn as a test set. It provides a more reliable performance estimate than a single train/test split. It is an essential practice to avoid deploying models that only work on development data.

Related terms

Machine Learning

Overfitting

Overfitting occurs when an ML model learns the training data too well, memorizing noise, and performs poorly on new data. It is like memorizing exam answers instead of understanding the subject. It is prevented with techniques such as cross-validation, regularization, and data augmentation. It is one of the most common risks in enterprise AI projects.

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

F1 Score

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.

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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