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Foundations of Artificial Intelligence & Machine Learning

Licence
OPEN CC-BY-4.0
Authors
Nidhi Sharma, Honey Singh, Ajay Sharma, Deepak Dagar
Published
2026-07-28 · Zenodo
Language
eng
Length
37166 words
Type
narrative text
Open ↗ Download Open original ↗
Decision Trees

Decision Trees are popular classification algorithms that represent decisions using tree-like structures. The algorithm divides data into branches based on conditions and feature values. Each branch represents a decision path leading toward a final classification outcome. For example, a student performance prediction system may classify results using conditions such as:

  • attendance percentage
  • assignment completion
  • examination scores Decision Trees are easy to understand because their structure resembles human decision-making processes.

Figure 5.5: Structure of a Decision Tree

The figure illustrates how a Decision Tree divides data into branches based on conditions until final classification decisions are reached. Advantages of Decision Trees Decision Trees are widely used because they are:

  • easy to interpret
  • simple to visualize
  • suitable for both numerical and categorical data They are especially useful in business analytics and medical decision-support systems where transparency is important. K-Nearest Neighbors (KNN) K-Nearest Neighbors is another commonly used classification algorithm. Instead of learning a mathematical equation, KNN classifies new data based on similarity with nearby data points. The algorithm identifies the nearest neighbors and assigns the most common category among them. For example, if most nearby customers belong to a “high-purchase” group, a new similar customer may also be classified into that category. KNN is simple but can become slower with very large datasets because it compares distances repeatedly. Support Vector Machine (SVM) Support Vector Machine is a powerful classification algorithm used for complex data classification tasks.

SVM attempts to find the best boundary, known as a hyperplane, that separates different categories with maximum distance. This method is highly effective in:

  • image classification

  • text categorization

  • handwriting recognition

  • bioinformatics SVM performs well in high-dimensional datasets and is widely used in Machine Learning research. Naive Bayes Classification Naive Bayes is a probabilistic classification algorithm based on Bayes’ Theorem. It calculates probabilities and predicts categories using statistical relationships between variables. Naive Bayes is especially effective in:

  • email spam detection

  • sentiment analysis

  • text classification

  • recommendation systems One major advantage of Naive Bayes is its speed and efficiency with large text datasets.