MACHINE LEARNING

A/B Testing

Machine Learning

Definition

A/B testing is an experimental method in which two versions (A and B) of an element are compared to determine which performs better. In the AI context, it is used to test different models, prompts, or strategies before adopting them in production. For SMEs, it is fundamental to validate that AI brings real improvements over the current process.

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.

Business & Strategy

KPI (Key Performance Indicator)

KPIs are key metrics that measure the performance of a process, department, or company against objectives. For AI projects, typical KPIs include: average response time, error rate, volume of documents processed, hours saved per month, conversion increase. Defining clear KPIs before implementing AI is essential to measure its success.

Business & Strategy

ROI (Return on Investment)

ROI measures the return on an investment relative to its cost. For AI projects, ROI is calculated by comparing benefits (time savings, error reduction, increased sales) with costs (licenses, development, training). A well-planned AI project for an SME should show positive ROI within 3-6 months of implementation.

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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