Alteryx Designer Desktop Knowledge Base

Welcome to the addictive world of predictive analytics. We have the perfect platform for you to start exploring your data.

If there is a space in the temporary directory, Predictive tools in Data Investigation, Predictive and Time Series and even R tool fail with errors like the following: Error in parse(text - 5): :1:17:unexpected INCOMPLETE_STRING

"Alteryx is not installed" error can happen if InstallInfo.ini is not found.

When using the R tool, packages can be downloaded from CRAN and installed, but it will be important that the version of R installed in Designer must be compatible with the package version.

When installing R you get the error "lib = c:/program files/Alteryx/R-3.6.3/library" is not writable

The following error is encountered in Linear Regression tool if null values exist.

This article details on the steps to read/extract password protected excel file in Alteryx Designer using the R code.

AlteryxRPluginEngine.dll could not be loaded: The specified procedure could not be found

Data preparation and investigation are a must for successful Predictive Modeling.

There are three model options in the Logistic Regression Tool; logit, probit, and cloglog. This post discusses which one is the right one for you.

A broad overview and introduction to what Decision Trees are, and how they work.

This article describes and explains the outputs of the Decision Tree Tool.

In statistics, standardization (sometimes called data normalization or feature scaling) refers to the process of rescaling the values of the variables in your data set so they share a common scale. Often performed as a pre-processing step, particularly for cluster analysis, standardization may be important to getting the best result in your analysis depending on your data. 

A common concern in predictive modeling is whether a model has been overfit. In statistics, overfitting refers to the phenomena when an analytical model corresponds too closely (or exactly) to a specific data set, and therefore may fail when applied to additional data or future observations. One common method that can be used to mitigate overfitting is regularization. Regularization places controls on how large the coefficients of the predictor variables grow.  In Alteryx, the option of implementing regularized regression is available for the Linear Regression and Logistic Regression Tools.

An overview and broad introduction to random forests, which are implemented by the Forest Model Tool in Alteryx. 

R is an open-source programming language and software environment, specifically intended for statistical computing and graphics. The Alteryx Predictive Tools install includes an installation of R, along with a set of R Packages used by the Predictive Tools. This article describes how to determine which R packages (and versions) are installed for used with your Alteryx R Tool, as well as a few Alteryx-specific packages on Github. 

An explanation of stochastic processes, pseudorandom number generators, and their existence in Alteryx.

Neural Networks are frequently referred to as "black box" predictive models. This is because the actual inner workings of why a Neural Network sorts data the way it does are not explicitly available for interpretation. A wide variety of work has been conducted to make Neural Networks more transparent, ranging from visualization methods to developing a Neural Network model that can “show it’s work”. This article demonstrates how to leverage the NeuralNetTools R package to create a plot of the Neural Network trained by the Alteryx Neural Net tool. 

Sampling weights, also known as survey weights, are positive values associated with the observations (rows) in your dataset (sample), used to ensure that metrics derived from a data set are representative of the population (the set of observations).

With the introduction of the Predictive Analytics Starter Kit, you can enhance your analytic skills through an interactive, guided starter kit that teaches core predictive modeling techniques (A/B testing, linear regression, and logistic regression)

Time series forecasting is using a model to predict future values based on previously observed values. In a time series forecast, the prediction is based on history and we are assuming the future will resemble the past. We project current trends using existing data.