Supervised Learning in Machine Learning
Introduction
Supervised learning is a machine learning technique where a model learns from labeled data. The dataset consists of input features \((x_1, x_2, x_3)\) and an output label \(y\). The goal is to find a function that maps inputs to correct outputs.
Features and Labels in a Dataset
A dataset consists of examples used to train a model. Each example contains:
- Features \((x_1, x_2, x_3)\): Input variables describing an observation.
- Label \(y\): The expected output for a given input.
Example dataset:
| Study Hours (x1) | Sleep Hours (x2) | Practice Tests (x3) | Exam Score (y) |
|---|---|---|---|
| 1 | 6 | 1 | 45 |
| 2 | 7 | 2 | 50 |
| 3 | 5 | 1 | 55 |
| 4 | 6 | 3 | 65 |
Supervised Learning Problem Setting
The goal is to find a function \( g(x) \) that predicts \( y \). Given a dataset:
\[ y = f(x_1, x_2, x_3) \]
A possible hypothesis function:
\[ y = 10x_1 + 5x_2 + 3x_3 + 20 \]
Example calculation:
\[ y = 10(4) + 5(6) + 3(3) + 20 = 99 \]
Mathematical Representation
Given \( N \) training examples:
\[ \{(x_{1},y_{1}),…,(x_{N},y_{N})\} \]
The function we seek:
\[ g: X \to Y \]
The learning algorithm minimizes a loss function to find the best \( g \).
Video
Summary
- Supervised learning learns from labeled data.
- It can be used for classification and regression.
- Common algorithms include Linear Regression and Neural Networks.
- Used in email filtering, medical diagnosis, etc.
Sources
- Introduction to Machine Learning by Andreas Müller & Sarah Guido
- Python and Machine Learning by Bernd Klein
Reach PostNetwork Academy
Thank You!