What is a supervised learning algorithm in C and how is it implemented?

Table of Contents

• Introduction
• Linear Regression Algorithm
  • What is Linear Regression?
  • Implementation Steps
  • Sample Implementation in C
• Conclusion

Introduction

Supervised Learning is a subset of machine learning where models are trained using labeled datasets. This approach allows the model to learn from input-output pairs, making it capable of predicting outputs for unseen inputs. Common supervised learning algorithms include linear regression, decision trees, and support vector machines. This article focuses on implementing a basic linear regression algorithm in C.

Linear Regression Algorithm

What is Linear Regression?

Linear regression is a statistical technique used to model the relationship between a dependent variable (output) and one or more independent variables (inputs). It predicts the output using a linear equation of the inputs.

Implementation Steps

1. Data Preparation: Gather a dataset with features and corresponding labels.
2. Model Representation: Represent the linear model as Y=wX+bY = wX + bY=wX+b, where YYY is the predicted output, XXX is the input vector, www is the weight, and bbb is the bias.
3. Cost Function: Define a cost function (Mean Squared Error) to measure the prediction error.
4. Optimization: Use an optimization method (e.g., Gradient Descent) to minimize the cost and adjust the model parameters.
5. Prediction: Use the trained model to predict outputs for new data.

Sample Implementation in C

Here’s a simple implementation of linear regression in C:

Conclusion

Supervised learning algorithms, such as linear regression, provide a robust framework for making predictions based on labeled data. The implementation in C involves defining the model, training it using gradient descent, and utilizing it for predictions. This example serves as a fundamental introduction to more complex supervised learning techniques and their real-world applications.