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    A decision tree is a supervised learning algorithm that classifies data by recursively splitting it into subsets based on feature values until reaching a decision at a leaf node. It works like a flowchart: internal nodes represent feature-based tests, branches represent outcomes of those tests, and leaves represent class predictions.

    Core working principle:

    1. Start at the root node with the full dataset.

    2. Select the best feature to split the data using a criterion such as Information Gain (ID3, C4.5) or Gini Impurity (CART).

    3. Split into subsets based on feature values.

    4. Repeat recursively for each subset until all samples in a node belong to the same class or no further split is possible.

    5. Assign class labels to leaf nodes.

    Key splitting criteria:

    • Information Gain: Measures reduction in entropy after a split. Higher gain means better separation.

    • Gini Impurity: Measures probability of incorrect classification. Lower Gini means purer nodes.

    Example in Python (CART with scikit-learn):

    from sklearn.tree import DecisionTreeClassifier, export_text
    from sklearn.datasets import load_iris
    from sklearn.model_selection import train_test_split

    # Load dataset
    X, y = load_iris(return_X_y=True)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)

    # Train Decision Tree
    clf = DecisionTreeClassifier(criterion='gini', max_depth=3, random_state=42)
    clf.fit(X_train, y_train)

    # Display tree rules
    print(export_text(clf, feature_names=load_iris().feature_names))

    # Evaluate
    print("Accuracy:", clf.score(X_test, y_test))
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    How do decision trees classify examples?
    Decision trees classify the examples by sorting them down the tree from the root to some leaf node, with the leaf node providing the classification to the example. Each node in the tree acts as a test case for some attribute, and each edge descending from that node corresponds to one of the possible answers to the test case.
    hackerearth.com
    Medium
    https://medium.com/data-science/decision-trees-for-classification-co…
    Decision Trees for Classification — Complete Example - Medium
  1. GeeksForGeeks
    https://www.geeksforgeeks.org › machine-learning › ...

    Decision Tree in Machine Learning - GeeksforGeeks

    Feb 23, 2026 · A decision tree is a supervised learning algorithm used for both classification and regression tasks. It has a hierarchical tree structure which consists of a root node, branches, internal …

    • A decision tree is a supervised learning algorithm used for both classification and regression tasks. It has a hierarchical tree structure which consists of a root no…

    See results only from geeksforgeeks.org
  2. Medium
    https://medium.com › data-science

    Decision Trees for Classification — Complete Example

    Jan 1, 2023 · In this article, we discussed a simple but detailed example of how to construct a decision tree for a classification problem and how it can be used to …

  3. scikit-learn
    https://scikit-learn.org › stable › modules › tree

    1.10. Decision Trees — scikit-learn 1.8.0 documentation

    Decision Trees (DTs) are a non-parametric supervised learning method used for classification and regression. The goal is to create a model that predicts the …

  4. DataCamp
    https://www.datacamp.com › tutorial › decision-tree-classification-python

    Decision Tree Classification in Python Tutorial - DataCamp

    1. Decision trees are easy to interpret and visualize.
    2. It can easily capture Non-linear patterns.
    3. It requires fewer data preprocessing from the user, for example, there is no need to normalize columns.
    4. It can be used for feature engineering such as predicting missing values, suitable for variable selecti…
    1. Decision trees are easy to interpret and visualize.
    2. It can easily capture Non-linear patterns.
    3. It requires fewer data preprocessing from the user, for example, there is no need to normalize columns.
    4. It can be used for feature engineering such as predicting missing values, suitable for variable selection.
    See more on datacamp.com
  5. mljourney.com
    https://mljourney.com › decision-tree-in-machine...

    Decision Tree in Machine Learning: How They Work

    Oct 29, 2025 · This example illustrates how decision trees naturally segment populations into meaningful groups, creating interpretable rules that loan officers …

  7. codelucky.com
    https://codelucky.com › decision-tree-algorithm

    Decision Tree Algorithm: Interpretable Classification …

    Learn everything about the Decision Tree Algorithm: an interpretable classification method in machine learning. Step-by-step explanation with examples, visuals, …

  8. Towards Data Science
    https://towardsdatascience.com › decision-tree...

    Decision Tree Classifier, Explained: A Visual Guide with …

    Aug 30, 2024 · A Decision Tree classifier creates an upside-down tree to make predictions, starting at the top with a question about an important feature in your …

  9. National Institute of Science Education and Research
    https://www.niser.ac.in › ~smishra › teach › lectures
    [PDF]

    Decision Trees : Classification and Regression Trees (CART) - NISER

    Let’s consider an example of using the CART algorithm for a binary classification problem. Suppose we have a dataset of patients with information about their age, gender, blood pressure, and cholesterol …

  10. HackerEarth
    https://www.hackerearth.com › practice › machine...

    Decision Tree Tutorials & Notes | Machine Learning

    Detailed tutorial on Decision Tree to improve your understanding of Machine Learning. Also try practice problems to test & improve your skill level.

  11. datamapu.com
    https://datamapu.com › posts › classical_ml › decision...

    Decision Trees for Classification - Example

    Dec 19, 2023 · Decision Trees are a powerful, yet simple Machine Learning Model. An advantage of their simplicity is that we can build and understand them step …

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