# AutoAI glossary #

Learn terms and concepts that are used in AutoAI for building and deploying machine learning models\.

**aggregate score**  
The aggregation of the four anomaly types: level shift, trend, localized extreme, variance\. A higher score indicates a stronger score\.

**algorithm**  
A formula applied to data to determine optimal ways to solve analytical problems\.

**anomaly prediction**  
An AutoAI time\-series model that can predict anomalies, or unexpected results, against new data\.

**AutoAI experiment**  
An automated training process that considers a series of training definitions and parameters to create a set of ranked pipelines as model candidates\.

**batch employment**  
Processes input data from a file, data connection, or connected data in a storage bucket and writes the output to a selected destination\.

**bias detection (machine learning)**  
To identify imbalances in the training data or prediction behavior of the model\.

**binary classification**  
A classification model with two classes and only assigns samples into one of the two classes\.

**classification model**  
A predictive model that predicts data in distinct categories\.

**confusion matrix**  
A performance measurement that determines the accuracy between a model’s positive and negative predicted outcomes to positive and negative actual outcomes\.

**cross validation**  
A technique that tests the effectiveness of machine learning models\. It is also used as a resampling procedure for models with limited data\.

**data imputation**  
Substituting missing values in a data set with estimated values\.

**exogenous features**  
Features that can influence the prediction model but cannot be influenced in return\. See also: Supporting features

**fairness**  
Determines whether a model produces biased outcomes that favor a monitored group over a reference group\. Fairness evaluations detect if the model shows a tendency to provide a favorable or preferable outcome more often for one group over another\. Typical categories to monitor are age, sex, and race\.

**feature correlation**  
The relationship between two features\. For example, postal code might have a strong correlation with income in some models\.

**feature encoding**  
Transforming categorical values into numerical values\.

**feature importance**  
The relative impact a particular column or feature has on the model's prediction or forecast\.

**feature scaling**  
Normalizing the range of independent variables or features in a data set\.

**feature selection**  
Identifying the columns of data that best support an accurate prediction or score\.

**feature transformation**  
In AutoAI, a phase of pipeline creation that applies algorithms to transform and optimize the training data to achieve the best outcome for the model type\.

**holdout data**  
Data used to test or validate the model's performance\. Holdout data can be a reserved portion of the training data, or it can be a separate file\.

**hyperparameter optimization (HPO)**  
The process for setting hyperparameter values to the settings that provide the most accurate model\.

**incremental learning**  
The process of training a model that uses data that is continually updated without forgetting data that is obtained from the preceding tasks\.

**large tabular data**  
Structured data that exceeds the limit on standard processing and must be processed in batches\. See incremental learning\.

**labeled data**  
Data that is labeled to identify the appropriate data vectors to be pulled in for model training\.

**monitored group**  
A class of data monitored to determine whether the results differ significantly from the results of the reference group\. For example, in a credit app, you might monitor applications in a particular age range and compare results to the age range more likely to recieve a positive outcome to evaluate whether there might be bias in the results\.

**multiclass classification model**  
A classification task with more than two classes\. For example, where a binary classification model predicts *yes* or *no* values, a multi\-class model predicts *yes*, *no*, *maybe*, or *not applicable*\.

**multivariate time series**  
Time series experiment that contains two or more changing variables\. For example, a time series model that forecasts the electricity usage of three clients\.

**optimized metric**  
The metric used to measure the performance of the model\. For example, accuracy is the typical metric that is used to measure the performance of a binary classification model\.

**pipeline (model candidate pipeline)**  
End\-to\-end outline that illustrates the steos in a workflow\.

**positive class**  
The class that is related to your objective function\.

**reference group**  
A group that you identify as most likely to receive a positive result in a predictive model\. You can then compare the results to a monitored group to look for potential bias in outcomes\.

**regression model**  
A model that relates a dependent variable to one or more independent variable\.

**scoring**  
In machine learning, the process of measuring the confidence of a predicted outcome\.

**supporting features**  
Input features that can influence the prediction target\. See also: Exogenus features

**text classification**  
A model that automatically identifies and classifies text into distinct categories\.

**time series model (AutoAI)**  
A model that tracks data over time\.

**trained model**  
A model that is ready to be deployed\.

**training**  
The initial stage of model building, involving a subset of the source data\. The model can then be tested against a further, different subset for which the outcome is already known\.

**training data**  
Data used to teach and train a model's learning algorithm\.

**univariate time series**  
Time series experiment that contains only one changing variable\. For example, a time series model that forecasts the temperature has a single prediction column of the temperature\.

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