When working with predictive models, the biggest challenge is not building a model but selecting the right one. A model might perform well on training data but fail miserably when exposed to unseen data, a classic case of overfitting. This is where cross-validation comes in, and yes, you can do it in Alteryx Designer without being a Python or R guru.
In this article, we’ll explore how Alteryx handles cross-validation and model selection, how to compare multiple predictive models, and what it looks like to deploy a model within the platform. We’ll also cover a pro tip on running pre-trained models.
And for today’s snack pairing? 🍇 Grapes.
Why grapes? Because, just like cross-validation, you don’t rely on just one grape—you taste a few before deciding if the bunch is good. Small samples that give you the bigger picture!
What is Cross-Validation?
Cross-validation is a model validation technique used to assess how well a model generalizes to unseen data. Instead of splitting your dataset into just train and test, cross-validation divides the data into multiple folds:
The data is split into k subsets (folds).
The model is trained on k-1 folds and tested on the remaining fold.
This process repeats k times so every fold is used once as a test set.
The results are averaged, giving a more reliable measure of performance.
Alteryx has this functionality baked in, meaning you don’t have to manually code loops or manage partitions like in Python’s sklearn.
Cross-Validation in Alteryx Designer
Alteryx predictive tools integrate cross-validation into the model training workflow. Here’s how it works:
Step 1: Input Data and Preparation
Use the Input Data Tool to bring in your dataset.
Clean with the Data Cleansing Tool, and optionally, Imputation Tool for missing values.
Split into target and predictors with the Select Tool.
Step 2: Training Models with Built-in Cross-Validation
Many predictive tools in Alteryx, such as Decision Tree, Forest Model, Linear Regression, and Logistic Regression, include a configuration option for validation method. Here, you can choose:
Simple Split Validation (training vs testing % split).
k-Fold Cross-Validation (recommended).
By enabling k-fold cross-validation, Alteryx automatically runs the model multiple times and averages the performance metrics. No extra configuration is needed—you just tick the box.
Step 3: Comparing Multiple Models
Alteryx makes it easy to train and compare multiple models in parallel. For instance:
Drag in both a Decision Tree Tool and a Logistic Regression Tool, connect them to the same data, and configure cross-validation.
Use the Model Comparison Tool to evaluate them side by side.
This tool lets you compare metrics like:
Accuracy
ROC curve and AUC (Area Under Curve)
R-squared for regression tasks
Mean Squared Error (MSE)
In practice, this means a non-technical analyst can test three or four models in minutes and select the winner without writing code.
From Model Selection to Deployment
Once you’ve selected the best-performing model, the next step is deployment. In Alteryx, this means scoring new data with the chosen model.
Connect your trained model to the Score Tool.
Feed in new (unlabeled) data through another input.
The tool outputs predictions alongside the original dataset.
Finally, you can write results back to a database, Excel, or even publish them as part of a scheduled workflow on Alteryx Server.
A Note on Model Deployment at Scale
While Alteryx makes training and scoring straightforward, you should keep in mind:
Models trained in Designer are often generalized for accessibility. They may not always match the precision of fine-tuned models in Python or R.
For large datasets or highly customized modeling, you might integrate Alteryx with Python or R directly using the Python Tool or R Tool.
Pro Tip: Running a Pre-Trained Model
Did you know you can use pre-trained models in Alteryx? For example, if a data scientist has already trained a logistic regression in Python, they can export the model as a PMML (Predictive Model Markup Language) file.
In Alteryx, you can:
Use the PMML Input Tool to bring in the pre-trained model.
Connect it to the Score Tool.
Score new data without retraining the model.
This is a powerful option for blending the best of both worlds:
Alteryx for ease-of-use, scoring, and deployment.
Python/R for advanced customization and fine-tuning.
Cross-Validation in Alteryx vs Python
Feature | Alteryx | Python (scikit-learn) |
|---|---|---|
Ease of use | Checkbox in tool config | Requires coding loops/functions |
Model comparison | Drag-and-drop Model Comparison Tool | Must code metrics manually |
Deployment | Built-in Score Tool | Must export model + custom scripts |
Flexibility | Limited to built-in models | Unlimited (custom algorithms |
In short, Alteryx makes model selection accessible for non-technical users, while Python gives advanced data scientists more control.
Why Cross-Validation Matters
Cross-validation ensures that the model you choose isn’t just a fluke. For a business analyst in Alteryx, it gives confidence that the model is robust. For a data scientist, it’s a sanity check before deployment.
Think of it as taste-testing different bunches of grapes before committing to a purchase. You don’t want sour results in production!
Final Thoughts
Alteryx brings the power of predictive modeling to the business analyst by simplifying complex processes like cross-validation, model comparison, and deployment into intuitive drag-and-drop workflows. While it won’t replace the customizability of Python or R, it dramatically lowers the barrier to entry.
And with the option to bring in pre-trained models, Alteryx sits comfortably between simplicity and flexibility - empowering analysts to make smarter, validated decisions without writing a single line of code.
So, next time you’re building a model in Alteryx, don’t just settle for a simple split. Try cross-validation, you’ll be glad you tested the whole bunch of grapes before biting into one! 🍇
Happy snacking and analyzing!