This site uses different types of cookies, including analytics and functional cookies (its own and from other sites). To change your cookie settings or find out more, click here. If you continue browsing our website, you accept these cookies.
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.
You want to impress your managers, so you decide to try some predictions on your data – forecasting, scoring potential marketing campaigns, finding new customers… That's great! Welcome to the addictive world of predictive analytics. We have the perfect platform for you to start exploring your data.
I know you want to dive right in and start testing models. It's tempting to just pull some data and start trying out tools, but the first and fundamentally most important part of all statistical analysis is the data investigation.
Your models won't mean much unless you understand your data. Here's where the Data Investigation Tools come in! You can get a statistical breakdown of each of your variables, both string and numeric, check for outliers (categorical and continuous), test correlations to slim down your predictors, and visualize the frequency and dispersion within each of your variables.
Part 1 of this article will give you an overview of the Field Summary Tool (never leave home without it!) Part 2 will touch on the Contingency and Frequency Tables, and Distribution Analysis; Part 3 will be the Association Analysis Tool, and the Pearson and Spearman Correlations; and Part 4 will be all the cool plotting tools.
Always, every day, literally every time you acquire a new data set, you will start with the Field Summary Tool. I cannot emphasize this enough, and I promise it will save you headaches.
There are three outputs to this tool: a data table containing your fields and their descriptive statistics, a static report, and the interactive visualization dashboard that provides a visual profile of your variables. From this output, you can select subsets to view, sort each of the panels, view and zoom in on specific values, and it even includes a visual indicator of data quality.
You'll get a nifty report with plots and descriptive statistics for each of your variables. Likely the most important part of this report is '% Missing' – ideally, you want 0.0% missing. If you are missing values, don't fret. You can remove these records or impute those values (another reason knowing your data is so important).
Also check 'Unique Values' – if you have a single unique value in one of your variables, that won't add anything useful to your model, so consider deselecting that variable.
The Remarks field is also very useful – it will suggest field-type changes for fields with a small number of unique values, perhaps that should be a string field. Or, if some values of your field have a small number of value counts, you may consider combining some value levels together.
The better YOU know your data, the more efficient and accurate your models will be. Only you know your data, your use case, and how your results are going to be applied. But we're here to help you get as familiar as you can with whatever data you have.
Stay tuned for subsequent articles – these tools will be your new best friends. Happy Alteryx-ing!
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.
With the Python Tool, Alteryx can manipulate your data using everyone’s favorite programming language - Python! Included with the tool are a few of pre-built libraries that extend past even the native Python download. This allows you to extend your data manipulation even further than one could ever imagine. The libraries installed are listed here - and below I’ll go into a bit more detail on what and why these libraries are so useful.
Each library is well documented, and there’s usually an introduction or examples on their sites to get you started on how a basic function in their library works.
ayx – Alteryx API – simply enough, we’re using Alteryx, sooo yea, kind of a requirement for the translation between Alteryx and Python.
jupyter – Jupyter metapackage – If you’ve used a Jupyter notebook in the past, you’ll notice the interface for the Python Tool is similar. This interface allows you to run sections of code outside of actually running the workflow, which makes understanding and testing your data that much easier.
matplotlib – Python plotting package – Any charting, plotting, or graphical needs you would want will be in this package. This provides a great deal of flexibility for whatever you want to visualize.
numPy – NumPy, array processing for numbers, strings, records, and objects – Native Python processes data in what some would call a cumbersome way. For instance, if you wanted to make a matrix, a.k.a. a 4x4 table, you would need to create a list within a list, which can slow processing a bit. However, NumPy has its own “array” type that fits the data in this matrix pattern that allows for faster processing. Additionally, it has a bunch of methods of handling numbers, strings, and objects that make processing a whole lot easier and a whole lot faster.
pandas – Powerful data structures for data analysis, time series, and statistics – This is your staple for handling data within Alteryx. Those who have used Python, but never pandas, will enter a whole new beautiful world of data handling and structure. Data manipulation within Python is faster, cleaner, and easier to code with. The best part about it is that the Python Tool will read in your Alteryx data as a pandas data frame! Understanding this library should be one of the first things to know when tackling the Python code.
requests – Python HTTP for Humans – for all the connector/Download Tool fans out there. If any of you are familiar with making HTTP requests (API calls and the like), then you should introduce yourselves to this package and explore how Python performs these requests.
scikit-learn – a set of Python modules for machine learning and data mining – Welcome to the world of machine learning in Python! This library is your go-to for statistical and predictive modeling and evaluation. Any crazy and wild methods you’ve learned for machine learning will most likely be found here and can really push the boundaries of data science.
scipy – Scientific Library for Python – all your scientific and technical computing can be found here. This library builds off the packages already installed here, like numPy, pandas, and matplotlib. Dealing with mathematical models and formulae are usually located within this library and can help provide that higher level analysis of your data.
six – Python 2 and 3 compatibility utilities – For those who are unfamiliar, Python versions come in 2 forms, version 2.x and 3.x (with 3.x being the most recent). Now, even though Python 3 is supposed to be the latest and greatest, there are still many users out there who prefer using Python 2. Therefore, integration between the two is a bit tricky with syntax differences, etc. The six module provides functions that are usable between the two so everyone can remain calm and happy! Their documentation is usually coupled with which version the functions most closely align to, so a user can get a better idea to its functionality.
SQLAlchemy – Database Abstraction Library – SQL in Python! Covers all your database needs from connecting to and extracting data, allowing it to interact with your Python code and thus, Alteryx itself.
statsmodels – statistical computations and models for Python – This library builds off sci-kit learn but focuses more on statistical tests and data exploration. Additionally, it utilizes R-style formulae with pandas data frames to fit models!
These are the libraries installed with the Python Tool, which can do almost any data function imaginable. Of course, if you’re looking to do something that these libraries don’t provide, there are myriad other Python libraries that I’m sure will help you with your use case. Most of these are also well documented in how to use so search away and let your mind float away in the beautiful cosmos created by Python.
As most of us can agree, predictive models can be extremely useful. Predictive models can help companies allocate their limited marketing budget on the most profitable group of customers, help non-profit organizations to find the most willing donors to donate to their cause, or even determine the probability a student will be admitted into a given school. A well-designed predictive model can help us make smart and cost-effective business decisions.
You may have run across this error, using the html plugin predictive tools (Linear Regression, Logistic Regression, Decision Tree):
Logistic Regression: Error in searchDir(dbDir, lang) : Logistic Regression: Expecting a single string value: [type=NULL; extent=0]
In 2018.2, this can happen when you have previously had an Admin version of Designer installed, but have since uninstalled. Once you've installed the 2018.2 non-Admin version with Predictive tools, these errors will now occur.
Help is on the way! (In the form of suggestions and an upcoming stable release.) You have several options. First, you can install an Admin version of Designer concurrently - 11.8, 2018.1, 2018.2, etc.
Last ditch effort: delete registry keys. This is not recommended - only delete keys if you cannot install a current version, or cannot wait until the next stable update.
Step 0) Save your license key somewhere easy to find: Options> Manage Licenses
Step 1) Open the Registry Editor (type regedit into your windows search bar) and delete the following directory:
Now, go predict stuff! Happy Alteryx-ing.
Logistic Regression is different from other types of regression because it creates predictions within a range of 0-1 and it does not assume that the predictor variables have a constant marginal effect on the target variable - making it applicable to many dichotomous problems including: estimating the probability that a student will graduate, the probability that a voter will vote for a specific candidate, or the probability that someone will respond to a marketing campaign.
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.
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
Overview: I wrote this as a short example into how one might use Alteryx to write a further Alteryx module to do complicated or repetitive tasks dynamically that would be difficult to do through the front end.
This module will automatically produce another Alteryx module that will do frequency statistics for a file. This should save the manual time (for files with lots of columns) adding a summarize for each column. It also saves transposing the file (which for large files is very slow to run). Instructions:
Change the input to that module to whichever file you like (or use Testing.yxmd which is provided)
Run it – this will create the Result.yxmd module
Open Result.yxmd – and change the input in the module to be the same file you used in step 2
Change the output if necessary (it defaults to an Alteryx database)
At the moment it does deal with &’s and single quotes in files, but won’t do anything clever like do stats on substrings for long fields.
I hope this inspires people to use this technique and build on the module I’ve built.
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.
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.
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.