Table 1 Selected relevant terms in Artificial Intelligence

Algorithm

A step-by-step procedure or set of rules for solving a problem or performing a task.

Neural Network

A computational model consisting of interconnected nodes (neurons) that process data.

Feature

An individual measurable property or characteristic used as input to a model.

Feature Engineering

The process of selecting, modifying, or creating features to improve the performance of a machine learning model.

Input

The data provided to an AI system or model to process and analyze.

Output

The result produced by an AI system or model after processing the input data.

Training Data

A dataset used to train an AI model, allowing it to learn patterns and relationships.

Testing Data

A dataset used to evaluate the performance of a trained AI model.

Supervised Learning

A machine learning approach where models are trained on labeled data, learning to map inputs to outputs.

Unsupervised Learning

A machine learning approach where models find patterns in unlabeled data without specific guidance on what to predict.

Reinforcement Learning

A machine learning paradigm where agents learn by interacting with an environment and receiving feedback through rewards or penalties.

Overfitting

When a model learns the training data too well, capturing noise or irrelevant data variables and reducing its ability to generalize to new data.

Underfitting

When a model is too simple to capture the underlying patterns in the data, resulting in poor performance on both training and testing data.

Bias

A systematic error introduced by a model, causing it to consistently favor certain outcomes or predictions.

Variance

The variability of model predictions across different datasets, often leading to overfitting if too high.

Hyperparameters

The settings or parameters of a machine learning algorithm that are set before training and control the learning process.