Simple decision tree maker

Kickstart your journey of finding a transformative solution to your problems with Edrawax, an easy-to-use and free online decision tree maker. Explore over 26,000 symbols and 2000+ examples to plot intricate decision trees.

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Free decision tree templates from EdrawMax

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Budget Analysis Decision Tree

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Consumer Purchase Decision Tree

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App Development Decision Tree

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Expected Value Analysis Decision Tree

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Statistical Probability Decision Tree

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Verbal Report Decision Tree

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Why Use EdrawMax Decision Tree Maker?

Free templates for every scenario

Edrawmax has a massive catalog of decision tree templates. They are excellent for classifying information, supervising learning algorithms, and establishing task distribution hierarchies. Head to the software, find a template, and kickstart your journey of plotting intricate decision trees.

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26,000+ professionally drawn symbols

EdrawMax enables users to make detailed and engaging decision trees with a vast symbol library. So, build connections, establish a hierarchy, and distribute nodes in seconds. With this, identifying loopholes and reducing clutter is a matter of a few clicks.

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Create decision tree slideshow in seconds!

No need to switch platforms for work presentations. Enable the EdrawMax presentation mode and select areas of your design to generate a slideshow in seconds. Press F5 to preview the slides and present your work.

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Create decision trees from any device

EdrawMax gives you the freedom to design anywhere. It supports cross-platform compatibility, for access to your work from Windows, Linux, Android, MacOS, and iOS. Still not impressed? Enjoy creating decision trees on EdrawMax Online from any device with active internet.

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How to Make a Decision Tree in 3 Simple Steps

What our users say

Elena Rodriguez, Senior Project Manager

Honestly, I was drowning in spreadsheets trying to map out our client onboarding. Everything else I tried was just too clunky or required a PhD to figure out. Wondershare’s decision tree maker is a total game changer. It’s super intuitive—literally just drag and drop, and you’re golden. My team actually understands the workflow now without me having to hop on a Zoom call every five minutes to explain it. If you’re tired of messy flowcharts that look like a bowl of spaghetti, this is a no-brainer.

David Thompson, Systems Architect

I’ve used a fair few diagramming tools in my time, and most of them are a bit of a faff when you’re trying to build out complex logic gates. This tool from Wondershare is spot on, though. It’s got a decent range of templates so you aren't staring at a blank screen, and the auto-layout feature saves me a massive amount of time. It’s streamlined my troubleshooting docs perfectly. No bells and whistles you don’t need, just a solid bit of kit that gets the job done properly.

Jamie Vance, Head of Customer Experience

Our support scripts were a hot mess before we started using this. We needed a way for our Tier 1 agents to navigate tricky refund policies without getting lost in the weeds. The decision tree builder is honestly so slick. It looks professional enough for our internal wiki, but it’s easy enough that even the new hires can follow the logic paths without a hitch. It’s definitely cut down our average handle time because the "choose your own adventure" style of the trees is just so clear. Love it.

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FAQs About EdrawMax Decision Tree Tools

What is a decision tree in machine learning?

A decision tree is a popular machine learning algorithm used for both classification and regression tasks. It visualizes decisions in a tree-like structure, where internal nodes represent tests on attributes, branches represent outcomes, and leaf nodes represent final predictions. It is highly intuitive and easy to interpret for human users.
How does a decision tree make a prediction?

To make a prediction, the algorithm starts at the root node and evaluates a specific feature. Based on the data's value, it follows the corresponding branch to the next node. This process repeats until it reaches a leaf node, which provides the final output or category for the input.
What is the difference between classification and regression trees?

Classification trees are used when the target variable is categorical, such as "yes" or "no." Regression trees are applied when the target variable is continuous, like predicting house prices. While both use a tree structure, they differ in how they calculate splits and measure the accuracy of their final predictions.
What are the main components of a decision tree?

A decision tree consists of three primary parts: the root node, which is the starting point; internal nodes, which represent decision points based on features; and leaf nodes, which represent the final outcomes. Branches connect these nodes, showing the path taken based on the specific criteria met during the process.
What is "pruning" in the context of decision trees?

Pruning is a technique used to reduce the size of a decision tree by removing branches that provide little predictive power. This helps prevent overfitting, where the model becomes too complex and performs poorly on new data. By simplifying the tree, pruning improves its ability to generalize to unseen information.