Free AI Decision Tree Maker
Transform decision logic into clear visual trees instantly. Describe your if-then-else scenarios and AI creates a professional decision tree diagram.
A bug triage decision tree
Real decision logic, real tree.
Input
What the AI produces
Decision trees are flowcharts where every node is a decision and every leaf is a final outcome — no looping, no merge points. Useful when you need to communicate "which priority does this bug get?"
What is a decision tree maker?
Describe the conditions and outcomes of a decision — "if a customer's last login was over 90 days ago and they have no open tickets, mark them at-risk" — and Flowova builds a tree with binary or n-ary branches and labeled leaves. Nested conditions, default cases, and explicit precedence are preserved. Best for human-decision trees (business rules, support triage, classification rubrics); machine-learning decision trees with split criteria and gini values are not the focus here.
Build a Decision Tree Step by Step
Describe Your Decision Logic
Describe the decisions and outcomes. The format that works best: a series of "if-then" branches with eventual leaf nodes (final outcomes).
- Plain English conditions and outcomes
- Binary or n-ary branches both work
- Default cases and 'else' fallback supported
AI Creates Your Tree
The AI builds a tree with the decision as the root, branches for each condition, and leaves for the final outcomes.
- Nested conditions become tree depth
- Branch labels match your wording verbatim
- Not for ML decision trees with split criteria
Customize and Export
Refine — decision trees often need rebalancing. Drag branches, restyle, and export.
- Refine outcome leaves inline
- Theme for runbook vs. customer-facing context
- Free PNG; SVG and Mermaid are Pro
Decision Tree Maker Features
Binary and N-ary Branches
Yes/No decisions render with two branches. Multi-way decisions ("category? billing/technical/sales") render with N labeled branches from one node.
Nested Conditions
"If A then if B then C" generates two-level depth. The tree depth is the natural recursion depth of your description.
Default and Else Branches
"Otherwise", "else", "default", "fallback" all parse as the catch-all branch from a multi-way decision node.
Leaf Outcomes Distinguished
Terminal outcomes ("Mark as P0", "Add to backlog") render as labeled end nodes with a distinct shape from intermediate decisions.
Business Rules and Triage
Targets human decision logic — customer triage, escalation paths, eligibility rules, troubleshooting trees, classification rubrics.
Not for ML Decision Trees
Decision trees with split criteria, gini values, or leaf class distributions (sklearn / XGBoost output) are not the target — use a model visualization tool.
When to use the decision tree maker
Use this tool for
- Bug or incident triage rules — 'which severity bucket does this go into' — that your on-call rotation needs to learn fast.
- Approval and eligibility policies where every input combination must land at exactly one outcome, with no loops or ambiguity.
- Troubleshooting paths and diagnostic flows — the kind on a printed lab card or a customer-support runbook.
- Decision documentation in playbooks where the reviewer just wants to know which leaf the answer ends up at.
Use a different tool for
- Flows that loop back or merge — decision trees are by definition acyclic. Use the Flowchart Maker for those.
- Sequential steps between decisions — same reason, a flowchart is the right shape.
- ML classification or regression trees from scikit-learn, XGBoost, or LightGBM — those have splits with thresholds and Gini scores; use the library's own graphviz or `plot_tree` export.
- Decisions tightly coupled to if-else code — If-Else to Flowchart accepts the code form directly.
Decision Tree Maker FAQ
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