Machine Learning Fundamentals: Core Algorithms and Techniques

Posted on Dec 4, 2025 in Computer Engineering

Rule-Based Classification Technique

Rule-based classification is a data mining method where IF–THEN rules are used to classify data into different categories. Each rule has two parts:

  • IF (condition): tests certain attribute values
  • THEN (conclusion): assigns a class label

Example:

IF age > 18 AND income = high THEN class = “Premium Customer”.

Key Points of Rule-Based Classification

  1. Simple and Interpretable: Rules are easy to understand because they are written in natural language.
  2. Classification Using Rule Sets: A set of rules is generated from the training data. When a new instance arrives, rules are matched in order (priority/decision list) or all matching rules vote for the class.
  3. Rule Generation: Rules can be created using algorithms like Decision Trees, RIPPER, or Apriori-like methods.
  4. Advantages:
    • Easy to implement and interpret
    • Handles both numerical and categorical data
    • Good for explaining decisions

Conclusion: Rule-based classification classifies data based on clear, interpretable IF–THEN rules, making it popular for applications where transparency and simplicity are important.

Principal Component Analysis (PCA)

Principal Component Analysis (PCA) is a statistical technique used for dimensionality reduction. It transforms a large set of correlated variables into a smaller set of uncorrelated variables called principal components, while retaining most of the important information (variance) in the data.

Key Concepts in PCA

  1. Reduces Dimensionality: PCA converts high-dimensional data into fewer dimensions by identifying the directions in which the data varies the most.
  2. Principal Components: These are new variables created as linear combinations of original variables.
    • First principal component (PC1): captures maximum variance.
    • Second principal component (PC2): captures the next highest variance, and so on. All components are orthogonal (independent).
  3. Steps in PCA:
    • Standardize the data
    • Compute covariance/correlation matrix
    • Calculate eigenvalues and eigenvectors
    • Select top components with highest eigenvalues
    • Transform data into the new component space
  4. Applications:
    • Data compression
    • Noise reduction
    • Visualization of high-dimensional data
    • Preprocessing for machine learning models

Conclusion: PCA simplifies complex datasets by converting them into a smaller number of meaningful components, helping in faster processing and better understanding of the data without losing much information.

Applications of Neural Networks

Neural Networks are widely used in many real-world domains because they can learn patterns from large and complex data. Some important applications include:

  1. Image Recognition and Computer Vision: Used in face recognition, object detection, medical image analysis (like identifying tumors), and autonomous driving.
  2. Natural Language Processing (NLP): Neural networks power applications such as speech recognition, machine translation, chatbots, sentiment analysis, and text generation.
  3. Finance and Business: Used for stock market prediction, credit scoring, fraud detection, customer behavior analysis, and risk management.
  4. Healthcare: Helps in disease diagnosis, drug discovery, patient monitoring, and predicting treatment outcomes using medical data.
  5. Robotics and Control Systems: Used in robot navigation, decision-making, adaptive control, and real-time learning systems.
  6. Gaming and AI Agents: Enable reinforcement learning agents to play games like Chess, Go, and video games at human or superhuman levels.

Conclusion: Neural networks are powerful tools that can learn from data, making them useful in fields like vision, language, finance, healthcare, and robotics.

Random Forest Algorithm: Ensemble Learning

Random Forest is an ensemble learning technique that builds multiple decision trees during training and combines their outputs to make a final prediction.

  • For classification: it takes the majority vote of all trees.
  • For regression: it takes the average of predictions.

Random Forest introduces randomness in two ways:

  1. Random sampling of data (bootstrap samples) for each tree.
  2. Random selection of features at each split.

This helps produce a strong model that reduces overfitting and improves accuracy.

Advantages of Random Forest

  1. High Accuracy: Combining many trees improves prediction accuracy and stability.
  2. Reduces Overfitting: Randomness in data and features helps prevent the model from learning noise.
  3. Handles Large Datasets: Works well with high-dimensional data and large feature sets.
  4. Works for Both Classification and Regression: Versatile and widely used for many types of problems.

Limitations of Random Forest

  1. Complex and Less Interpretable: With many trees, it becomes difficult to understand how decisions are made (black-box nature).
  2. Computationally Heavy: Requires more memory and time compared to a single decision tree, especially with many trees.
  3. Slower Prediction: Making predictions involves evaluating multiple trees, which can be slow in real-time systems.
  4. May Not Perform Well on Very Sparse Data: For extremely sparse, high-dimensional datasets, simpler models may work better.

Decision Tree Algorithm and Attribute Selection

A Decision Tree is a supervised learning algorithm used for classification and regression. It works by splitting the dataset into smaller subsets based on the values of attributes, forming a tree-like structure.

How the Decision Tree Works

  1. Select the best attribute to split the data.
  2. Create branches for each possible value of the attribute.
  3. Recursively repeat the process for each subset.
  4. Stop when all records in a node belong to the same class or no further split is possible.

The final model has:

  • Root Node: The first split.
  • Internal Nodes: Tests on attributes.
  • Leaf Nodes: Class labels.

Decision trees are easy to understand and interpret because they mimic human decision-making.

Attribute Selection Measures (ASMs)

These measures decide which attribute is best for splitting the dataset at each step. Common ASMs include:

1. Information Gain (IG)

  • Based on Entropy from Information Theory.
  • Measures how much uncertainty is reduced after the split.
  • Attribute with the highest IG is selected.
  • Used in algorithms like ID3.

2. Gain Ratio

  • Overcomes the problem of Information Gain favoring attributes with many values.
  • Normalizes IG by considering the intrinsic value of a split.
  • Attribute with the highest Gain Ratio is chosen.
  • Used in C4.5.

3. Gini Index

  • Measures impurity of a node.
  • Lower Gini means purer node.
  • Attribute with the lowest Gini Index is selected.
  • Used in CART (Classification and Regression Tree).

Conclusion: The Decision Tree algorithm creates a simple, interpretable classification model. Attribute selection measures like Information Gain, Gain Ratio, and Gini Index help choose the best attribute at each split, improving the accuracy and efficiency of the tree.