Predictive R-based tools

The Predictive category includes R-based tools for general predictive modeling for both classification and regression models, as well as tools for model comparison and for hypothesis testing relevant for predictive modeling. See Download and Use Predictive 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.