A subset of artificial intelligence that enables systems to learn from data and improve over time without explicit programming.

A statistical technique that uses historical data to predict future outcomes, often applied in sales forecasting.

The process of cleaning and transforming raw data into a suitable format for analysis, enhancing model performance.

Assessing a predictive model's performance using specific metrics to ensure reliability and accuracy.

A type of machine learning where models are trained on labeled data to make predictions.

A type of machine learning that identifies patterns in data without labeled outcomes.

A data preprocessing technique that standardizes the range of independent variables to improve model performance.

A technique for assessing how the results of a statistical analysis will generalize to an independent dataset.

The process of optimizing model parameters to improve performance, often using techniques like grid search.

A statistical method used to model the relationship between a dependent variable and one or more independent variables.

A supervised learning technique that categorizes data into predefined classes or labels.

Identifying and handling unusual data points that can skew model results.

The graphical representation of data to identify trends, patterns, and insights effectively.

Quantitative measures used to assess the performance of predictive models, such as accuracy and F1 score.

A subset of data used to train a machine learning model.

A separate subset of data used to evaluate the performance of a trained machine learning model.

A table used to evaluate the performance of a classification model, showing true vs. predicted classifications.

A graphical representation of a model's diagnostic ability, plotting true positive rates against false positive rates.

The degree to which a human can understand the cause of a decision made by a model.

The condition of a dataset, determined by its accuracy, completeness, consistency, and reliability.

A process where the output of a system is used as input for future iterations, enhancing model refinement.

A repetitive approach in model building where feedback is used to make continuous improvements.

A set of rules allowing different software entities to communicate, often used to integrate machine learning models.

The practical use of machine learning techniques in business scenarios, such as sales forecasting.

The study of moral implications and responsibilities in the development and use of machine learning technologies.