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# Alteryx Knowledge Base

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## Analyzing Partial Effects in Alteryx

If you are building a predictive model, inevitably you will want to analyze the effect that your independent variables have on your dependent variable. This article is meant to shed some light on the Alteryx-specific options for this type of analysis!

## An Introduction to Sampling Weights

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).

## Use Case: Using Additional R Packages: Times Series Clustering Example

Alteryx has a full set of integrated predictive tools but even with developers working at full  speed , it is hard to keep up with the R community. Sometimes users want to install and utilize their  favorite  R packages. This post demonstrates how to use and install additional R packages.

## How to Save Your Predictive Model

How to save your predictive model.

## What is the Set Seed argument, and Why is it There?

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

## Performing Regression Analysis using Alteryx Designer

Regression analysis is widely used for prediction and forecasting. Alteryx customers use these statistical tools to understand risk, fraud, customer retention and pricing, among many other business needs.

## Performing Time Series Forecasting in Alteryx Designer

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.

## How to Create a Plot of an Alteryx Neural Network Model

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.

## Planting Seeds: An Introduction to Decision Trees

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

## Tool Mastery | K-Centroids Diagnostics

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.

## Tool Mastery | K-Centroids Cluster Analysis

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.

## Tool Mastery | Forest Model

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.

## Standardization in Cluster Analysis

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.

## Tool Mastery | Neural Network

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.

## Tool Mastery | Field Summary

The  Field Summary Tool  analyzes data and creates a summary report containing descriptive statistics of data in selected columns. It’s a great tool to use when you want to make sure your data is structured correctly before using any further analysis, most notably with the suite of models that can be generated with the Predictive Tools.

## R Packages in the R Tool

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.