MACHINE LEARNING

Overfitting

Machine Learning

Definition

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.

Related terms

Machine Learning

Underfitting

Underfitting occurs when a model is too simple to capture patterns in the data, producing poor performance on both training data and new data. The solution is to use a more complex model, add features, or increase training data. Balancing overfitting and underfitting is the central challenge of machine learning.

Machine Learning

Cross-Validation

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.

Machine Learning

Data Augmentation

Data augmentation is the technique of artificially expanding a dataset by applying transformations to existing data. For images: rotations, zoom, lighting variations. For text: paraphrasing, synonyms, back-translation. It is crucial when you have few training data, a common situation for SMEs starting their AI journey.

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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