Decision Tree Feature Importance

Rank the columns of your data by how much a tree depends on them.

Try:

Data as CSV with a header row

sepal_length,sepal_width,petal_length,petal_width,species
5.1,3.5,1.4,0.2,setosa
4.9,3.0,1.4,0.2,setosa
4.7,3.2,1.3,0.2,setosa
4.6,3.1,1.5,0.2,setosa
5.0,3.6,1.4,0.2,setosa
5.4,3.9,1.7,0.4,setosa
4.6,3.4,1.4,0.3,setosa
5.0,3.4,1.5,0.2,setosa
4.4,2.9,1.4,0.2,setosa
4.9,3.1,1.5,0.1,setosa
5.4,3.7,1.5,0.2,setosa
4.8,3.4,1.6,0.2,setosa
4.8,3.0,1.4,0.1,setosa
4.3,3.0,1.1,0.1,setosa
5.8,4.0,1.2,0.2,setosa
5.7,4.4,1.5,0.4,setosa
5.4,3.9,1.3,0.4,setosa
5.1,3.5,1.4,0.3,setosa
5.7,3.8,1.7,0.3,setosa
5.1,3.8,1.5,0.3,setosa
5.4,3.4,1.7,0.2,setosa
5.1,3.7,1.5,0.4,setosa
4.6,3.6,1.0,0.2,setosa
5.1,3.3,1.7,0.5,setosa
4.8,3.4,1.9,0.2,setosa
5.0,3.0,1.6,0.2,setosa
5.0,3.4,1.6,0.4,setosa
5.2,3.5,1.5,0.2,setosa
5.2,3.4,1.4,0.2,setosa
4.7,3.2,1.6,0.2,setosa
4.8,3.1,1.6,0.2,setosa
5.4,3.4,1.5,0.4,setosa
5.2,4.1,1.5,0.1,setosa
5.5,4.2,1.4,0.2,setosa
4.9,3.1,1.5,0.2,setosa
5.0,3.2,1.2,0.2,setosa
5.5,3.5,1.3,0.2,setosa
4.9,3.6,1.4,0.1,setosa
4.4,3.0,1.3,0.2,setosa
5.1,3.4,1.5,0.2,setosa
5.0,3.5,1.3,0.3,setosa
4.5,2.3,1.3,0.3,setosa
4.4,3.2,1.3,0.2,setosa
5.0,3.5,1.6,0.6,setosa
5.1,3.8,1.9,0.4,setosa
4.8,3.0,1.4,0.3,setosa
5.1,3.8,1.6,0.2,setosa
4.6,3.2,1.4,0.2,setosa
5.3,3.7,1.5,0.2,setosa
5.0,3.3,1.4,0.2,setosa
7.0,3.2,4.7,1.4,versicolor
6.4,3.2,4.5,1.5,versicolor
6.9,3.1,4.9,1.5,versicolor
5.5,2.3,4.0,1.3,versicolor
6.5,2.8,4.6,1.5,versicolor
5.7,2.8,4.5,1.3,versicolor
6.3,3.3,4.7,1.6,versicolor
4.9,2.4,3.3,1.0,versicolor
6.6,2.9,4.6,1.3,versicolor
5.2,2.7,3.9,1.4,versicolor
5.0,2.0,3.5,1.0,versicolor
5.9,3.0,4.2,1.5,versicolor
6.0,2.2,4.0,1.0,versicolor
6.1,2.9,4.7,1.4,versicolor
5.6,2.9,3.6,1.3,versicolor
6.7,3.1,4.4,1.4,versicolor
5.6,3.0,4.5,1.5,versicolor
5.8,2.7,4.1,1.0,versicolor
6.2,2.2,4.5,1.5,versicolor
5.6,2.5,3.9,1.1,versicolor
5.9,3.2,4.8,1.8,versicolor
6.1,2.8,4.0,1.3,versicolor
6.3,2.5,4.9,1.5,versicolor
6.1,2.8,4.7,1.2,versicolor
6.4,2.9,4.3,1.3,versicolor
6.6,3.0,4.4,1.4,versicolor
6.8,2.8,4.8,1.4,versicolor
6.7,3.0,5.0,1.7,versicolor
6.0,2.9,4.5,1.5,versicolor
5.7,2.6,3.5,1.0,versicolor
5.5,2.4,3.8,1.1,versicolor
5.5,2.4,3.7,1.0,versicolor
5.8,2.7,3.9,1.2,versicolor
6.0,2.7,5.1,1.6,versicolor
5.4,3.0,4.5,1.5,versicolor
6.0,3.4,4.5,1.6,versicolor
6.7,3.1,4.7,1.5,versicolor
6.3,2.3,4.4,1.3,versicolor
5.6,3.0,4.1,1.3,versicolor
5.5,2.5,4.0,1.3,versicolor
5.5,2.6,4.4,1.2,versicolor
6.1,3.0,4.6,1.4,versicolor
5.8,2.6,4.0,1.2,versicolor
5.0,2.3,3.3,1.0,versicolor
5.6,2.7,4.2,1.3,versicolor
5.7,3.0,4.2,1.2,versicolor
5.7,2.9,4.2,1.3,versicolor
6.2,2.9,4.3,1.3,versicolor
5.1,2.5,3.0,1.1,versicolor
5.7,2.8,4.1,1.3,versicolor
6.3,3.3,6.0,2.5,virginica
5.8,2.7,5.1,1.9,virginica
7.1,3.0,5.9,2.1,virginica
6.3,2.9,5.6,1.8,virginica
6.5,3.0,5.8,2.2,virginica
7.6,3.0,6.6,2.1,virginica
4.9,2.5,4.5,1.7,virginica
7.3,2.9,6.3,1.8,virginica
6.7,2.5,5.8,1.8,virginica
7.2,3.6,6.1,2.5,virginica
6.5,3.2,5.1,2.0,virginica
6.4,2.7,5.3,1.9,virginica
6.8,3.0,5.5,2.1,virginica
5.7,2.5,5.0,2.0,virginica
5.8,2.8,5.1,2.4,virginica
6.4,3.2,5.3,2.3,virginica
6.5,3.0,5.5,1.8,virginica
7.7,3.8,6.7,2.2,virginica
7.7,2.6,6.9,2.3,virginica
6.0,2.2,5.0,1.5,virginica
6.9,3.2,5.7,2.3,virginica
5.6,2.8,4.9,2.0,virginica
7.7,2.8,6.7,2.0,virginica
6.3,2.7,4.9,1.8,virginica
6.7,3.3,5.7,2.1,virginica
7.2,3.2,6.0,1.8,virginica
6.2,2.8,4.8,1.8,virginica
6.1,3.0,4.9,1.8,virginica
6.4,2.8,5.6,2.1,virginica
7.2,3.0,5.8,1.6,virginica
7.4,2.8,6.1,1.9,virginica
7.9,3.8,6.4,2.0,virginica
6.4,2.8,5.6,2.2,virginica
6.3,2.8,5.1,1.5,virginica
6.1,2.6,5.6,1.4,virginica
7.7,3.0,6.1,2.3,virginica
6.3,3.4,5.6,2.4,virginica
6.4,3.1,5.5,1.8,virginica
6.0,3.0,4.8,1.8,virginica
6.9,3.1,5.4,2.1,virginica
6.7,3.1,5.6,2.4,virginica
6.9,3.1,5.1,2.3,virginica
5.8,2.7,5.1,1.9,virginica
6.8,3.2,5.9,2.3,virginica
6.7,3.3,5.7,2.5,virginica
6.7,3.0,5.2,2.3,virginica
6.3,2.5,5.0,1.9,virginica
6.5,3.0,5.2,2.0,virginica
6.2,3.4,5.4,2.3,virginica
5.9,3.0,5.1,1.8,virginica

Commas, tabs or semicolons. Numeric columns split on a threshold; text columns get one branch per value. Rows with empty cells are skipped.

Target column (what to predict)

Split quality

Max depth

1 to 12

Min rows to split

2 or more

Test share %

0 to 50

Source: Fisher's Iris data (R. A. Fisher, 1936), 150 flowers, from the UCI Machine Learning Repository. Public domain facts.

Test accuracy

91.1%

The tree was grown on 105 rows and checked on 45 rows it never saw (a fixed, repeatable split). A big gap between training accuracy (98.1%) and test accuracy is the sign of overfitting.

Training accuracy: 98.1%; 105 rows
Test accuracy: 91.1%; 45 rows
Depth: 4; 13 nodes, 7 leaves
Root impurity: 1.5846; entropy
Root information gain: 0.9275
Classes: 3; setosa, versicolor, virginica

Tree

Each box shows the split, the entropy value, the number of rows, the count of rows per class ([setosa, versicolor, virginica]) and the majority class. Tap a box to fold its branch. Scroll sideways on a small screen.

Feature importance

petal_length64.1%
petal_width33%
sepal_width1.7%
sepal_length1.3%

Importance is the share of the total impurity reduction that each feature's splits produced, weighted by the rows they covered.

The tree as rules

Show 19 lines of if-then rules

if petal_length <= 2.6:
  predict setosa (36 rows)
if petal_length > 2.6:
  if petal_width <= 1.75:
    if petal_width <= 1.45:
      predict versicolor (23 rows)
    if petal_width > 1.45:
      if sepal_width <= 2.6:
        predict virginica (3 rows)
      if sepal_width > 2.6:
        predict versicolor (10 rows)
  if petal_width > 1.75:
    if petal_length <= 4.85:
      if sepal_length <= 6.05:
        predict versicolor (1 rows)
      if sepal_length > 6.05:
        predict virginica (1 rows)
    if petal_length > 4.85:
      predict virginica (31 rows)

About Decision Tree Feature Importance

This page builds an entropy tree on the iris data and shows the feature importance chart, the share of the total impurity reduction that each measurement is responsible for. It is free, needs no sign-up and runs in your browser, so nothing you enter is uploaded.

Every split lowers impurity by some amount, weighted by the number of rows it covers. A feature's importance is the sum over its splits divided by the total, so the importances add to 100%. On the iris flowers the two petal measurements carry nearly all of it, because they separate the three species far better than the sepal measurements.

Importance describes this tree, not the real world. Correlated features can share or steal importance from each other, and a feature a tree never splits on can still be informative. Treat it as a guide to which columns to look at first.

How to use Decision Tree Feature Importance

Pick a sample dataset, or paste your own CSV with a header row.
Choose the target column, the one you want to predict. It should have a handful of classes.
Choose Gini impurity or entropy, and set the maximum depth and the minimum number of rows needed to split a node.
Set a test share, for example 20%, to hold some rows back and measure accuracy on unseen data.
Read the accuracy figures, then look at the tree. Tap a box to fold its branch, or scroll sideways on a small screen.
Check the feature importance chart, and open the rules to copy the tree as text.

Features

Numeric columns split on a threshold; text columns get one branch per value.
Gini impurity or entropy as the split quality measure.
Maximum depth, minimum rows to split and a repeatable train and test split.
A tree diagram in responsive SVG with foldable branches, scrollable on phones.
Feature importance as the share of total impurity reduction.
Root impurity and information gain, so you can check a textbook calculation.
The tree as if-then rules, which you can copy.
Iris, a Titanic subset and play-tennis built in, with their sources stated.

Worked examples

Iris, entropy, depth 4, 30% test gives petal_length 64.1%, petal_width 33%, sepal_width 1.7%, sepal_length 1.3%
Test accuracy 91.1%.

Privacy: how it works

Your CSV is parsed and the tree is grown in your browser tab. Nothing is sent to Ulkaa or anyone else.

Tips

Remove ID columns, names and anything that would not be known when you need to predict.
Compare Gini and entropy on your data. They usually pick the same splits and differ only in the details.
Fold the branches of a big tree and read the top levels first. Those hold the strongest signals.
The test split is random but repeatable, so the same settings always give the same numbers.

Frequently asked questions

Is it really free?

Yes. There is no price, no trial and no credit card. Ulkaa Tool does the work with your own device, so there is no server bill to pass on to you.

Do I need an account?

No. There is no sign-up and no login. Open the page and use it. An Ulkaa account is not needed for anything on this tool.

Does it run in my browser?

Yes. Your CSV data are processed by code running in your browser tab, on your own device.

Is my data uploaded?

No. Your CSV data are never sent to Ulkaa or to anyone else. You can check this yourself by watching the Network tab in your browser developer tools while you use the tool.

How is decision tree feature importance calculated?

Add up the weighted impurity decrease of every split on the feature, then divide by the total decrease over the tree.

Are importances reliable?

They describe one tree. Correlated features can split the credit between them, and a high-cardinality feature can look more important than it is.

Tree

The tree as rules

Show 19 lines of if-then rules

if petal_length <= 2.6:
  predict setosa (36 rows)
if petal_length > 2.6:
  if petal_width <= 1.75:
    if petal_width <= 1.45:
      predict versicolor (23 rows)
    if petal_width > 1.45:
      if sepal_width <= 2.6:
        predict virginica (3 rows)
      if sepal_width > 2.6:
        predict versicolor (10 rows)
  if petal_width > 1.75:
    if petal_length <= 4.85:
      if sepal_length <= 6.05:
        predict versicolor (1 rows)
      if sepal_length > 6.05:
        predict virginica (1 rows)
    if petal_length > 4.85:
      predict virginica (31 rows)

About Decision Tree Feature Importance

Decision Tree Feature Importance

Test accuracy

Tree

Feature importance

The tree as rules

About Decision Tree Feature Importance

How to use Decision Tree Feature Importance

Features

Worked examples

Privacy: how it works

Tips

Frequently asked questions

Is it really free?

Do I need an account?

Does it run in my browser?

Is my data uploaded?

How is decision tree feature importance calculated?

Are importances reliable?

More Decision Tree Builder pages

Decision Tree Feature Importance

Test accuracy

Tree

Feature importance

The tree as rules

About Decision Tree Feature Importance

How to use Decision Tree Feature Importance

Features

Worked examples

Privacy: how it works

Tips

Frequently asked questions

Is it really free?

Do I need an account?

Does it run in my browser?

Is my data uploaded?

How is decision tree feature importance calculated?

Are importances reliable?

More Decision Tree Builder pages