Alteryx Designer Knowledge Base

Definitive answers from Designer experts.
This article details on the steps to read/extract password protected excel file in Alteryx Designer using the R code.
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AlteryxRPluginEngine.dll could not be loaded: The specified procedure could not be found
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Data preparation and investigation are a must for successful Predictive Modeling.
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This tool provides a number of different univariate time series plots that are useful in both better understanding the time series data and determining how to proceed in developing a forecasting model.
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The TS Filler Tool takes a data stream of time series data and fills in any gaps in the series.
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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.
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The humble histogram is something many people are first exposed to in grade school. Histograms are a type of bar graph that display the distribution of continuous numerical data. Histograms are sometimes confused with bar charts, which are plots of categorical variables.
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A broad overview and introduction to what Decision Trees are, and how they work.
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This article describes and explains the outputs of the Decision Tree Tool.
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The subtitle to this article should be a short novel on configuring the Decision Tree Tool in Alteryx. The initial configuration of the tool is very simple, but it you chose to customize the configuration of the tool at all, it can get complicated quickly. In this article, I am focusing on the configuration of the Tool. However, because it is a Tool Mastery, I am covering everything within the configuration of the tool
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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. 
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Typically the first step of Cluster Analysis in Alteryx Designer, the K-Centroids Diagnostics Tool assists you to in determining an appropriate number of clusters to specify for a clustering solution in the K-Centroids Cluster Analysis Tool, given your data and specified clustering algorithm. Cluster analysis is an unsupervised learning algorithm, which means that there are no provided labels or targets for the algorithm to base its solution on. In some cases, you may know how many groups your data ought to be split into, but when this is not the case, you can use this tool to guide the number of target clusters your data most naturally divides into.
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Clustering analysis has a wide variety of use cases, including harnessing spatial data for grouping stores by location, performing customer segmentation or even insurance fraud detection. Clustering analysis groups individual observations in a way that each group (cluster) contains data that are more similar to one another than the data in other groups. Included with the Predictive Tools installation, the K-Centroids Cluster Analysis Tool allows you to perform cluster analysis on a data set with the option of using three different algorithms; K-Means, K-Medians, and Neural Gas. In this Tool Mastery, we will go through the configuration and outputs of the tool.
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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.
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The Append Cluster Tool is effectively a Score Tool for the K-Centroids Cluster Analysis Tool. It takes the O anchor output (the model object) of the K-Centroids Cluster Analysis Tool, and a data stream (either the same data used to create the clusters, or a different data set with the same fields), and appends a cluster label to each incoming record. This Tool Mastery reviews its use. 
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An overview and broad introduction to random forests, which are implemented by the Forest Model Tool in Alteryx. 
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The Neural Network Tool in Alteryx implements functions from the nnet package in R to generate a type of neural networks called multilayer perceptrons. By definition, neural network models generated by this tool are feed-forward (meaning data only flows in one direction through the network) and include a single Hidden Layer. In this Tool Mastery, we will review the configuration of the tool, as well as what is included in the Object and Report outputs.
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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. 
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The Alteryx Forest Tool implements a random forest model using functions in the randomForest R package. Random forest models are an ensemble learning method that leverages the individual predictive power of decision trees into a more robust model by creating a large number of decision trees (i.e., a "forest") and combining all of the individual estimates of the trees into a single model estimate. In this Tool Mastery, we will be reviewing the configuration of the Forest Model Tool, as well as its outputs. 
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An explanation of stochastic processes, pseudorandom number generators, and their existence in Alteryx.
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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. 
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