Predictive R-based tools

Boosted Model Tool: The Boosted Model tool provides generalized boosted regression models based on the gradient boosting methods of Friedman. It works by serially adding simple decision tree models to a model ensemble so as to minimize an appropriate loss function.

Count Regression Tool: The Count Regression tool estimates regression models for count data using Poisson regression, quasi-Poisson regression, or negative binomial regression. The R functions used to accomplish this are glm() (from the R stats package) and glm.nb() (from the MASS package).

Cross-Validation Tool: The Cross-Validation tool compares the performance of one or more Alteryx-generated predictive models using the process of cross-validation. It supports all classification and regression models with the exception of Naive Bayes.

DataRobot Automodel Tool: The DataRobot Automodel tool uploads data to the DataRobot machine learning platform.

Decision Tree Tool: The Decision Tree tool predicts a target variable using one or more variables that are expected to have an influence on the target variable.

Deploy Tool: The Deploy tool uploads models directly to the Promote platform.

Forest Model Tool: The Forest Model tool predicts a target variable using one or more variables that are expected to have an influence on the target variable.

Gamma Regression Tool: The Gamma Regression tool relates a Gamma distributed, strictly positive variable of interest (target variable) to one or more variables (predictor variables) that are expected to have an influence on the target variable.

Lift Chart Tool: The Lift Cart tool produces two very commonly used charts of this type, the cumulative captured response chart (also called a gains chart) and the incremental response rate chart.

Linear Regression Tool: The Linear Regression tool relates a variable of interest (a target variable) to one or more variables that are expected to have an influence on the target variable.

Logistic Regression Tool: The Logistic Regression tool relates a binary (e.g., yes/no) variable of interest (a target variable) to one or more variables that are expected to have an influence on the target variable.

Model Coefficients Tool: The Model Coefficients tool extracts the model coefficients from a standard Alteryx Count, Gamma, Linear, or Logistic Regression model for use in customized reports or downstream calculations.

Model Comparison Tool: The Model Comparison tool compares the performance of one or more different predictive models based on the use of a validation (or test) dataset.

Naive Bayes Classifier Tool: The Naive Bayes Classifier tool creates a binomial or multinomial probabilistic classification model of the relationship between a set of predictor variables and a categorical target variable.

Nested Test Tool: The Nested Test tool examines whether two models, one of which contains a subset of the variables contained in the other, are statistically equivalent in terms of their predictive capability.

Network Analysis Tool: The Network Analysis tool creates an interactive visualization of a network, along with summary statistics and distribution of node centrality measures.

Neural Network Tool: The Neural Network tool allows a user to create a feedforward perceptron neural network model with a single hidden layer.

Score Tool: The Score tool takes as inputs an R model object produced by the Logistic Regression, Decision Tree, Forest Model, or Linear Regression macro and a data stream that is consistent with the model object and outputs the data stream with a score field appended to the data stream.

Spline Model Tool: The Spline Model tool predicts a variable of interest (target variable) based on one or more predictor variables using the two-step approach of Friedman's multivariate adaptive regression (MARS) algorithm.

Stepwise Tool: The Stepwise Regression tool makes use of both backward variable selection and mixed backward and forward variable selection.

Support Vector Machine Tool: The Support Vector Machine tool is used for classification problems, and accommodates instances where the data is considered linearly non-separable.

Survival Analysis Tool: The Survival Analysis tool generates a survival model that can be used by the Survival Score tool to estimate relative risk and restricted mean survival time.

Survival Score Tool: The Survival Score tool provides both the estimated relative risk and restricted mean survival time based on a Cox proportional hazards model, which can be estimated using the Survival Analysis tool.

Test of Means Tool: The Test of Means tool compares the difference in mean values (using a Welch two sample t-test) for a numeric response field between a control group and one or more treatment groups.

Variance Inflation Factors Tool: The Variance Inflation Factors tool produces a coefficient summary report that includes either the variance inflation factor or a generalized version of the VIF (GVIF) for all variables except the model intercept.