Decision Tree Classification in Machine Learning (2026)

Decision Trees (DTs) are non-parametric supervised learning models used for both classification and regression tasks. They learn simple decision rules based on the input features and use those rules to predict a target label or value.

In 2026, decision trees are still a core building block in many machine learning pipelines. They are easy to interpret, work well with tabular data, and form the basis of powerful ensemble methods such as random forests and gradient-boosted trees.

What is a decision tree?

A decision tree is a flowchart-like structure made of nodes and edges. Each internal node represents a question about a feature, each branch represents an answer to that question, and each leaf node represents a final prediction.

During training, the algorithm searches for the best question to ask at each node. Here, “best” means the split that makes the resulting child nodes as pure as possible with respect to the target classes.

Entropy: measuring impurity

To decide how good a split is, we need a way to measure how mixed a node is. One of the most common metrics for this is entropy, a concept borrowed from information theory.

You can think of entropy in several equivalent ways: It measures the average amount of information needed to describe the outcome.

It captures how unpredictable or surprising the labels in a node are.

It is low when a node is pure (mostly one class) and high when classes are balanced.

In decision trees, we treat entropy as a measure of impurity inside a node. The learning algorithm tries to reduce entropy at every split so that leaves become as pure and as informative as possible.

Formally, the entropy $H$ of a discrete random variable $X$ with possible values $x$ and probabilities $p(x)$ is

$H(X) = - \sum_{x} p(x) \log_2 p(x)$

In a classification tree, we compute entropy using the class probabilities inside a node. If a node contains only one class, its entropy is $0,$ and the node is perfectly pure.

Information gain: choosing the best split

When we split a node, we want the child nodes to have lower entropy than the parent. The information gain measures how much entropy decreases after a split.

Information gain is defined as the difference between the entropy of the parent node and the weighted sum of the entropies of the child nodes. The weights come from the proportion of samples that go to each child.

Mathematically, for a target $Y$ and a candidate splitting feature $X$:

$IG(Y, X) = H(Y) - \sum_{x \in unique(X)} P(x \mid X) \times H(Y \mid X = x)$Wherere:

• $H(\cdot)$ is the entropy,
• $Y$ is the set of labels in the parent node,
• $X$ is the feature that we split on,
• $x$ runs over the unique values of $X$,
• $Y[X = x]$ is the subset of labels that fall into the child node defined by $X = x$.

The training algorithm tries different features and thresholds at each node and keeps the split that maximizes information gain:

$X^{*} = \arg\max_{X_i} IG(Y, X_i)$

Worked example of entropy and information gain

Consider a parent node with $21$ samples: $11$ labeled Blue and $10$ labeled Yellow. The class probabilities are

$P(Y = Blue) = \frac{11}{21}, \quad P(Y = Yellow) = \frac{10}{21}$

The entropy of the parent node is

$H(parent) = - \frac{11}{21} \log_2 \frac{11}{21} - \frac{10}{21} \log_2 \frac{10}{21} \approx 0.998$

Now, suppose we split on a feature $X$ that can be either Square or Circle, as in the illustration below.

Entropy of the child nodes

For the Square child node, there are $9$ samples, with $7$ Blue and $2$ Yellow. The entropy is

$H(Y \mid X = Square) = - \frac{7}{9} \log_2 \frac{7}{9} - \frac{2}{9} \log_2 \frac{2}{9} \approx 0.764$

For the Circle child node, there are $12$ samples, with $4$ Blue and $8$ Yellow. The entropy is

$H(Y \mid X = Circle) = - \frac{4}{12} \log_2 \frac{4}{12} - \frac{8}{12} \log_2 \frac{8}{12} \approx 0.918$

Next, we compute the weighted average entropy after the split. The weights are the fraction of samples in each child node:

$H(Y \mid X) = \frac{9}{21} \times 0.764 + \frac{12}{21} \times 0.918 \approx 0.852$

Information gain of the split

Finally, we calculate the information gain:

$IG(parent, X) = H(parent) - H(Y \mid X) \approx 0.998 - 0.852 = 0.146$

Because the information gain is positive, this split reduces entropy and makes the child nodes purer than the parent. Among all candidate splits at this node, the algorithm chooses the one with the highest information gain.

When to stop splitting

Decision tree training is recursive. Every time we split a node, we can try to split its children again, which can lead to very deep trees.

To prevent the tree from growing without control and overfitting the training data, we use stopping criteria such as the following:

Pure node: if a node has entropy $H(node) = 0$, all samples belong to the same class, so there is no benefit in splitting further.

*Maximum depth: We can set a maximum depth for the tree. Once this depth is reached, the algorithm stops splitting, even if the nodes are not fully pure.

Minimum samples per node: We can enforce a minimum number $N$ of samples required to split a node. If a node has fewer than $N$ samples, it becomes a leaf.

At the end of training, nodes that have no children are called leaves. Each leaf predicts the class that appears most frequently among the samples that reach it.

Advantages and limitations

Decision trees are popular because they are easy to explain and visualize. Each prediction can be traced back to a path of simple if-then rules, which is valuable in domains where interpretability matters.

They also handle both numerical and categorical features and require little preprocessing. However, deep trees can overfit, capturing noise in the training data instead of general patterns.

To reduce these limitations, practitioners often use regularization, such as limiting tree depth or the minimum number of samples per leaf. Another common strategy is to combine many trees into ensembles like random forests or gradient-boosted trees.

Decision trees in modern machine learning workflows (2026)

In 2026, decision trees remain a reliable baseline for tabular datasets and structured business data. They are widely available in open-source libraries and integrate well with modern machine learning pipelines.

For example, libraries such as scikit-learn provide optimized implementations of decision tree classifiers, along with tools for cross-validation, feature engineering, and model evaluation.

To get hands-on guided experience, you can explore these ideas in the Data Science and AI course at Code Labs Academy and apply decision trees to real datasets.

Learn more

To put these concepts into practice, try implementing a decision tree classifier on a small dataset. Visualize the resulting tree and inspect how entropy and information gain drive each split.

Design the future of automation and intelligence through Machine Learning at Code Labs Academy and build end-to-end projects with decision trees and other powerful algorithms.