--- schema_version: "1" id: e103abc74ad037990bbc1cc2ee29d5bbf1c6b6e3caca2febedf799f52ac3c862 title: Einstein Discovery Glossary collection: admin version: current locale: en-us portal_url: https://docs.salesforce.com/admin/current/en-us/analytics/264-0-0/bi_edd_glossary.md canonical_url: https://help.salesforce.com/s/articleView?id=analytics.bi_edd_glossary.htm&release=264.0.0&type=5 source_site: help.salesforce.com content_type: documentation content_hash: e8a66cc7e2dd69cb48c06abc96602ba0f03cb127f00672d5a854258ba2af9bd0 description: Familiarize yourself with terminology that is commonly associated with Einstein Discovery. products: - CRM Analytics - Cross Cloud release: "264" --- # Einstein Discovery Glossary Familiarize yourself with terminology that is commonly associated with Einstein Discovery. Actionable Variable An _actionable variable_ is an explanatory variable that people can control, such as deciding which marketing campaign to use for a particular customer. Contrast these variables with explanatory variables that can’t be controlled, such as a customer’s street address or a person’s age. If a variable is designated as actionable, the model uses prescriptive analytics to suggest actions (improvements) the user can take to improve the _predicted_ outcome. Actual Outcome An _actual outcome_ is the real-world value of an observation's outcome variable after the outcome has occurred. Einstein Discovery calculates model performance by comparing how closely predicted outcomes come to actual outcomes. An actual outcome is sometimes called an _observed outcome_. Algorithm See _modeling algorithm_. Analytics Dataset An _Analytics dataset_ is a collection of related data that is stored in a denormalized, yet highly compressed, form. The data is optimized for analysis and interactive exploration. Attribute See _variable_. Average In Einstein Discovery, the _average_ represents the statistical mean for a variable. Bias If Einstein Discovery detects _bias_ in your data, it means that variables are being treated unequally in your model. Removing bias from your model can produce more ethical and accountable models and, therefore, predictions. See _disparate impact_. Binary Classification Use Case The _binary classification_ use case applies to business outcomes that are binary: categorical (text) fields with only two possible values, such as win-lose, pass-fail, public-private, retain-churn, and so on. These outcomes separate your data into two distinct groups. For analysis purposes, Einstein Discovery converts the two values into Boolean true and false. Einstein Discovery uses logistic regression to analyze binary outcomes. Binary classification is one of the main use cases that Einstein Discovery supports. Compare with _multiclass classification_. Cardinality _Cardinality_ is the number of distinct values in a category. Variables with high cardinality (too many distinct values) can result in complex visualizations that are difficult to read and interpret. Einstein Discovery supports up to 100 categories per variable. You can optionally consolidate the remaining categories (categories with fewer than 25 observations) into a category called **Other**. Null values are put into a category called **Unspecified**. Categorical Variable A _categorical variable_ is a type of variable that represents qualitative values (categories). A model that represents a binary or multiclass classification use case has a categorical variable as its outcome. See _category_. Category A _category_ is a qualitative value that usually contains categorical (text) data, such as Product Category, Lead Status, and Case Subject. Categories are handy for grouping and filtering your data. Unlike measures, you can’t perform math on categories. In Salesforce Help for Analytics datasets, categories are referred to as _dimensions_. Causation _Causation_ describes a cause-and-effect relationship between things. In Einstein Discovery, causality refers to the degree to which variables influence each other (or not), such as between explanatory variables and an outcome variable. Some variables can have an obvious, direct effect on each other (for example, how price and discount affect the sales margin). Other variables can have a weaker, less obvious effect (for example, how weather can affect on-time delivery). Many variables have no effect on each other: they are independent and mutually exclusive (for example, win-loss records of soccer teams and currency exchange rates). It's important to remember that you can’t presume a causal relationship between variables based simply on a statistical correlation between them. In fact, correlation provides you with a _hint_ that indicates further investigation into the association between those variables. Only with more exploration can you determine whether a causal link between them really exists and, if so, how significant that effect is. Coefficient A _coefficient_ is a numeric value that represents the impact that an explanatory variable (or a pair of explanatory variables) has on the outcome variable. The coefficient quantifies the change in the mean of the outcome variable when there’s a one-unit shift in the explanatory variable, assuming all other variables in the model remain constant. Comparative Insight _Comparative insights_ are insights derived from a model. Comparative insights reveal information about the relationships between explanatory variables and the outcome variable in your story. With comparative insights, you isolate factors (categories or buckets) and compare their impact with other factors or with global averages. Einstein Discovery shows waterfall charts to help you visualize these comparisons. Correlation A _correlation_ is simply the _association_—or “co-relationship”—between two or more things. In Einstein Discovery, correlation describes the statistical association between variables, typically between explanatory variables and an outcome variable. The strength of the correlation is quantified as a percentage. The higher the percentage, the stronger the correlation. However, keep in mind that correlation is _not_ causation. Correlation merely describes the strength of association between variables, not whether they causally affect each other. Count A _count_ is the number of observations (rows) associated with an analysis. The count can represent all observations in the dataset, or the subset of observations that meet associated filter criteria. Dataset See _Analytics dataset_. Date Variable A _date variable_ is a type of variable that contains date/time (temporal) data. Dependent Variable See _outcome variable_. Deployment Wizard The _Deployment Wizard_ is the Einstein Discovery tool used to deploy models into your Salesforce org. Descriptive Insights _Descriptive insights_ are insights derived from historical data using descriptive analytics. Descriptive insights show what happened in your data. For example, Einstein Discovery in Reports produces descriptive insights for reports. Diagnostic Insights _Diagnostic insights_ are insights derived from a model. Whereas descriptive insights show _what_ happened in your data, diagnostic insights show _why it happened_. Diagnostic insights drill deeper into correlations to help you understand which variables most significantly impacted the business outcome you’re analyzing. The term _why_ refers to a high statistical correlation, not necessarily a causal relationship. Disparate Impact If Einstein Discovery detects _disparate impact_ in your data, it means that the data reflects discriminatory practices toward a particular demographic. For example, your data can reveal gender disparities in starting salaries. Removing disparate impact from your model can produce more accountable and ethical insights and, therefore, predictions that are fair and equitable. Dominant Values If Einstein Discovery detects _dominant values_ in a variable, it means that the data is unbalanced. Most values are in the same category, which can limit the value of the analysis. Drift Over time, a deployed model's performance can _drift_, becoming less accurate in predicting outcomes. Drift can occur due to changing factors in the data or in your business environment. Drift also results from now-obsolete assumptions built into the story on which the model is based. To remedy a model that has drifted, you can refresh it by adjusting story settings, retraining it on newer data, and redeploying it. Duplicates If Einstein Discovery detects a _duplicate_ condition in your data, it means that two or more explanatory variables are highly correlated (for example, City and Postal Code). These variables have a _duplicate impact_ on the outcome. Einstein Discovery recommends choosing just one variable to improve results. Consider keeping the most descriptive field (for example, City) to make insights more easily interpretable. This condition is also known as _multicollinearity_. Ethical Use Ethical use reflects the application of AI and machine learning for fair and unbiased purposes. With Einstein Discovery, it's the practice of producing ethical and accountable stories, insights, and predictions. For an overview, take the [Responsible Creation of Artificial Intelligence](https://trailhead.salesforce.com/en/content/learn/modules/responsible-creation-of-artificial-intelligence) Trailhead module. Explanatory Variable An _explanatory variable_ is a variable that you explore to determine whether, and to what degree, it can influence the outcome variable for your story. Einstein Discovery calculates statistical associations between explanatory variables and the outcome variable. Based on the strength of the association, you can investigate further whether and how that explanatory variable affects the outcome variable. An explanatory variable is sometimes called an _input variable_, a _feature_, a _predictor variable_, or an _independent variable_. Feature See _explanatory variable_ or _predictor variable_. Feature Selection _Feature selection_ involves choosing the optimum set of features (explanatory or predictor variables) in a model. Ideally, a model contains the number of explanatory variables that best explain variations in the outcome variable. A model with too few explanatory variables can be too vague to detect underlying patterns in the data, resulting in an underfitting model. A model with too many explanatory variables can be overly specific and too complex to filter out noise in the data, resulting in an overfitting model. Successful feature selection includes the most influential explanatory variables with no significant lurking variables (important explanatory variables that are missing from the model). First-Order Analysis In an insight, a _first-order analysis_ examines how one explanatory variable explains variation in the outcome variable. First-order analysis is sometimes called _bivariate analysis_. Generalized Linear Model (GLM) _General Linear Model_ is a regression-based modeling algorithm that Einstein Discovery uses to build a model. Goal A _goal_ specifies the desired outcome for your model. A model’s goal includes its outcome variable plus your preferred direction (minimize or maximize) for the outcome. For example, your goal could be to maximize margin or to minimize costs. Einstein Discovery uses the model goal to orient its analysis and explain the insights it uncovered from the analysis. Gradient Boosting _Gradient Boosting_ is a decision tree-based ensemble machine learning algorithm that Einstein Discovery uses to build a model. Also called _Gradient Boosting Machine_ (or _GBM_). Hint See _signal_. Identical Values If Einstein Discovery detects _identical values_ in your data, it means that all values for a variable belong to the same category. Having identical values increases complexity—but no benefit—to the analysis of your data. Importance _Importance_ is the relative influence of a variable on the model's predicted outcome. In Einstein Discovery, importance indicates how much the model chooses to use a variable when predicting the outcome. The level of importance is quantified as a percentage. The higher the percentage, the greater the impact. Importance is an advanced metric that considers interactions between variables. If two variables are highly correlated and contain similar information, the model chooses the better variable to use. For example, when predicting energy usage the variables of temperature and number of air conditioner hours on are both highly correlated, but only one of the variables receives a high importance score. Improvement An _improvement_ is a suggested action, based on prescriptive analytics, that a user can take to improve the likelihood of a desired outcome. Improvements are associated with _actionable variables_, which are explanatory variables that people can control. Taking a suggested action can improve the _predicted_ outcome. An improvement is analogous to a _prescription_ in prescriptive analytics. Imputation Imputation is a statistical technique for replacing missing numeric values with values that are derived from another subset of your data. With imputation enabled, observations with missing values are safely counted during analysis. Independent Variable See _explanatory variable_ or _predictory variable_. Insight An _insight_ is a finding in your data. When you create a model, Einstein Discovery analyzes the data in your dataset and generates insights based on its analysis. Insights provide a starting point for you to investigate the relationships among your model’s explanatory variables and its goal. k-fold Cross-Validation Model validation process in which Einstein Discovery randomly divides all the observations in the Analytics dataset into four separate partitions of equal size. Next, it completes four test passes (folds) in which three of the partitions serve as the training set and one partition serves as the test set. For each fold, Einstein Discovery compiles model metrics, then averages the metrics for all four folds. Leakage _Leakage_ occurs when the data used to train your model includes one or more variables that contain the information that you’re trying to predict. This can result in models that are extremely accurate when, in actuality, they are problematic. To remedy data leakage, remove any variables from your model that are causing the leakage. Linear Regression In Einstein Discovery, _linear regression_ is an analytical technique used for the numeric use case. Logistic Regression In Einstein Discovery, _logistic regression_ is an analytical technique used for the binary classification use case. Lurking Variable A _lurking variable_ is an explanatory variable that is missing from your model but that significantly explains variations in the outcome variable. Mean A _mean_ is the statistical average: the sum of all items divided by the number of items. Measure A numeric value that quantifies something. See _numeric variable_. Model A _model_ is the sophisticated, custom equation based on a comprehensive, statistical understanding of past outcomes used to predict future outcomes. A model accepts the values of one or more predictor variables as input and produces a predicted outcome as output, along with top factors and improvements (if requested). Einstein Discovery walks you through the steps to create a model based on the outcome you want to improve (your model’s goal), the data you’ve assembled for that purpose (in the Analytics dataset), and other settings that tell Einstein Discovery how to conduct the analysis and communicate its results. Modeling Algorithm A _modeling algorithm_ is what Einstein Discovery uses to create a model. Einstein Discovery uses one of several algorithms: generalized linear model (GLM) is a regression-based algorithm, while gradient boosting machine (GBM) and XGBoost are decision tree-based machine learning algorithms. Model Manager The _Model Manager_ is the Einstein Discovery tool used to manage predictions and models you’ve deployed. Model Performance _Model performance_ are the metrics used to describe the performance of the predictive model. These metrics (quality indicators, which are sometimes called _fit statistics_) show how well the model's predictions fit the training data in the dataset. For definitions of quality indicators shown in ModelPerformance, see [Evaluate Model Quality](https://help.salesforce.com/s/articleView?id=analytics.bi_edd_model_metrics.htm&release=264.0.0&type=5 "Model performance metrics reveal quality measures and associated details for a model. Use model performance to evaluate a model's ability to predict an outcome. When ready, you then deploy a model to Salesforce to predict outcomes in production."). Multiclass Classification Use Case The _multiclass classification_ use case addresses a business outcome that has between 3 and 10 outcome values, such as five possible service plans or eight possible insurance policies. Multiclass classification is one of the main use cases that Einstein Discovery supports. Compare with _Binary Classification_. Noise _Noise_ is any data that doesn’t meaningfully explain variations in your outcome variable. See _signal_. Numerical Variable A _numerical variable_ is a type of variable that represents quantitative values (numbers), such as revenue or price. You can do math on numeric variables, such as calculating the total revenue or the average price. A numeric value always has an associated unit of measure, such as currency, volume, or weight. A model that represents a numeric use case has a numeric outcome variable. In the Analytics dataset documentation, a numeric column is referred to as a _measure_. Numerical Use Case In Einstein Discovery, the _numerical use case_ applies to model outcome variables that are numeric. Predicting a number field is a regression problem with its own set of metrics to measure model quality. Einstein Discovery uses linear regression to analyze numeric outcomes. The numerical use case is one of the main use cases that Einstein Discovery supports. Observation An _observation_ represents an instance of the data you want to analyze. An observation is analogous to a row of data in a CRM Analytics dataset, or to a record in a Salesforce object. For example, if your model’s goal is to maximize opportunity wins, then each observation represents a single opportunity. Occurrences See _count_. Outcome An _outcome_ is the business result you’re trying to analyze or predict. An outcome is typically a key performance indicator (KPI), such as sales margin or opportunity wins. Outcome Variable In a model, the _outcome variable_ is the column selected as the single, primary focus for analysis and predictions. The goal of a model is to maximize or minimize its outcome variable. An outcome variable is sometimes referred to as the _response_, the _target variable_, or the _dependent variable_. Outlier If Einstein Discovery detects _outliers_ in your data, it means that a variable contains data points that are unusually distant from the average value (more than five times the standard deviation from the mean for that variable). Uncommonly large or small numbers, potentially from data entry errors or rare events, affect averages (means) and standard deviations, which can reduce the accuracy of insights or predictions. Outliers can be selectively excluded from a model. Overfitting In predictive analytics, _overfitting_ occurs when a model performs well in predicting outcomes on the training data in the dataset, but less well when predicting outcomes for other data, such as production data. Using too many explanatory variables can result in an overly complex predictive model that captures the noise in your data. To mitigate overfitting, Einstein Discovery uses ridge regression and regularization. See also _underfitting_. Performance For predictive models, performance is a qualitative measure of how accurately a model predicts outcomes. Einstein Discovery provides model metrics that measure model performance in different ways. Predicted Outcome A prediction. Einstein Discovery calculates model performance by comparing how closely predicted outcomes come to actual outcomes. Prediction In Einstein Discovery, a prediction is a derived value (produced by a model), that represents a possible future outcome. You can think of a prediction as the output of a predictive model that is based on the inputs of predictor variables that the model accepts. Prediction Definition In Einstein Prediction Service, a parent resource that contains one or more models. If a prediction definition contains multiple models, then each model produces predictions for a different segment of the data. Prediction Field A _prediction field_ is a field where Einstein stores prediction scores for a Salesforce object. During deployment, Einstein can create this field automatically (called an _automated prediction field_), or a custom prediction field can be created later if needed. Predictive Analytics _Predictive analytics_ is the practice of analyzing historical and current data, based on AI, machine learning, predictive modeling, and statistical techniques. Einstein Discovery uses predictive analytics to identify patterns and predict probabilistic future outcomes. Predictive Model See _model_. Predictor or Predictor Variable A variable that a model expects as input. A prediction request passes values for each predictor variable that the model requires. Based on the provided input values, the model's equation produces a prediction as output. Predictors are also known as _features_ and _independent variables_. Prescription See _improvement_. Prescriptive Analytics _Prescriptive analytics_ is the practice of suggesting actions to improve predicted outcomes. Proxy Variable A _proxy variable_ is an explanatory variable that is highly correlated to another explanatory variable in relation to the outcome variable. When a proxy variable, such as a loan applicant's street address, is highly correlated to a protected characteristic, such as ethnicity, it can reflect discriminatory practices that compromise your analysis and predictions with unwanted bias. Einstein Discovery helps you identify proxy variables so that you can remove them, and the bias they reflect, from consideration in your models, insights, and predictions. R2 _R2_ measures a regression's model's ability to explain variation in the outcome. It represents the proportion of the variance in the outcome variable that is predictable from one or more explanatory variables. In general, the higher the R2, the better the model predicts outcomes. R2 is a commonly used metric for numeric use cases. Ranked Data _Ranked data_ is used to distribute data by probability. Also known as cumulative fraction, ranked data is often presented as deciles or quantiles. In Einstein Discovery, gain and lift charts are plotted on an x-axis of percentage of ranked data. For example, a ranked data of 0.1 equates to the top decile, or the 10% of records with the highest scores. Recommended Updates When analyzing your data, Einstein Discovery looks for issues, such as outliers or duplicates, that can decrease the value of the analysis. If detected, Einstein Discovery presents you _recommended updates_ to fix these data issues in your model. Residual A _residual_ is the mathematical difference between the observed (or actual) value and the predicted value. It's calculated as `residual=observed value-predicted value`. Residuals are also known as errors, and are used to assess model quality and accuracy. A positive residual means the prediction was too low, while a negative residual means the prediction was too high. Response See _outcome variable_. Ridge Regression _Ridge regression_ is a regularization approach that Einstein Discovery uses to mitigate model overfitting by preventing coefficients from getting too large. Sampling Technique of randomly selecting a subset of observations to analyze for the purpose of reducing the time needed to analyze the data. The sample should be large enough to be sufficiently representative of the variability in the data. Score (noun) A prediction associated with an observation. (verb) The process of predicting outcomes for a set of observations. Second-Order Analysis In an insight, a _second-order analysis_ examines how the combination of two explanatory variables explains variation in the outcome variable. In second-order analysis, the combined impact of both variables together on the outcome is sometimes called the _interaction effect_. Second-order analysis is sometimes called _multivariate analysis_. Segment A _segment_ is a subset of observations (rows) that meet the criteria specified in the segment filter. See _segmentation_. Segmentation Segmentation involves filtering your data to focus your prediction on a particular group, such as a customer type or region. Sensitive Variable A sensitive variable contains data that could potentially be associated with unfair treatment. Some examples are variables associated with race, gender, religion, national origin, sexual orientation, disability, or age. Less obvious examples include proxy variables, such as street address or ZIP code, which can reflect discriminatory practices. Einstein Discovery uses sensitive variables to detect potential bias in your data. Signal Signal is an indication of a statistically significant and potentially meaningful pattern in your data. For example, an insight can describe a high correlation between an explanatory variable and the outcome variable. By investigating the relationship further, you can learn whether the correlation helps explain variations in the outcome (possible signal) or not (possible noise). Sometimes referred to as a _hint_. Data Insights _Data insights_ are a collection of descriptive and diagnostic analytics that Einstein Discovery generates based on the data and model settings. Model Setup Wizard The _model setup wizard_ is the Einstein Discovery tool used to define your model settings, such as the model goal, data selections, and other preferences. Einstein Discovery uses these model settings to analyze the data, produce insights, and generate predictions. Strongest Predictor If Einstein Discovery detects a _strongest predictor_ in your data, it means that a variable explains the most variation in the data. Remove the variable if there’s an obvious mathematical relationship between it and the outcome (for example Cost and Price). Similarly, remove the variable if it known only after the outcome is known (for example, Reason for Churn in a customer churn analysis). Excluding strongest predictor variables can expose more subtle patterns in your data. Terminal State Data that is finalized and not expected to change. An example of finalized data is the date on which an order shipped. A record that has reached its terminal state represents an _actual_ outcome (also called _observed outcome_). Define the conditions under which your model’s outcome variable has attained its terminal state. Text Variable See _categorical variable_, _binary outcome_. Threshold In a binary classification model, the _threshold_ value tells your model how to classify a binary outcome. If the calculated probability is above the threshold value, Einstein classifies the outcome one way (such as True or Positive). If the calculated probability is below the threshold value, Einstein classifies the outcome the other way (such as False or Negative). The default threshold is 0.5, but you can tune this value up or down to accommodate your use case. The threshold is sometimes called the _Classification Threshold_ or _Decision Threshold_. Top Predictors _Top predictors_ are the conditions that most significantly drive the predicted outcome, in decreasing order of magnitude. A condition is a data value associated with a column. In Einstein Discovery, a predictor consists of one or two conditions. See _predictor variables_. Training Set In predictive analytics, the _training set_ is the portion of the data in your dataset that Einstein Discovery uses to train your model to make predictions. See also _validation set_. Underfitting In predictive analytics, _underfitting_ occurs when a model performs poorly in predicting outcomes on the training data in the dataset. Underfitting is often a result of an excessively simple model in which there aren't enough variables for a statistical algorithm to capture the underlying patterns in the data. See also _overfitting_. Unstructured Text Free-form text that varies in content and length. Examples include customer comments, survey feedback, social media postings, text messages, and emails. Contrast with _categorical variable_. Validation Set In predictive analytics, the _validation set_ is the portion of the data in your dataset that Einstein Discovery uses to validate the predictions generated by your trained model. See also _training set_. Variable A _variable_ represents a characteristic of the data you’re analyzing. A variable is analogous to a column in a dataset or a field in a Salesforce object. For example, an opportunity has variables—such as the opportunity type, lead source, fiscal year, lead source, expected amount—that describe properties associated with each opportunity. Each variable has one data type (number, text, or date). Einstein Discovery analyzes relationships among two types of variables: outcome variables and explanatory variables. Data scientists sometimes refer to variables as _attributes_ or _features_. XGBoost _XGBoost_ is a decision tree-based, ensemble machine learning algorithm that Einstein Discovery uses to build a model.