UNIT II SUPERVISED LEARNING - Introduction

SUPERVIS
ED
LEARNING
Presented By
Assistant Professor
AI&DS
Rathinam Technical Campus
kowsysara.soc@rathinam.in

Supervised Learning
Labeled Data: Supervised learning uses labeled datasets to train models.
Prediction & Classification: It helps in tasks like regression (predicting values) and classification
(categorizing data).

Supervised Learning
Definition:
Supervised Learning is a type of machine learning where an algorithm is trained on labeled data,
meaning each input is paired with the correct output. The model learns patterns from this data to
make predictions or classifications on new, unseen data.

Types of Supervised Learning in Machine
Learning

Regression Algorithms (For Predicting
Numbers)
Linear Regression – Finds a straight-line relationship.
Eg: Predicts house prices based on size.
Polynomial Regression – Fits curves instead of straight lines.
Eg:Predicts car mileage based on engine size.

Regression Algorithms (For Predicting
Numbers)
Ridge Regression – Prevents overfitting using regularization.
Eg: Predicting house prices where all features (size, location, no. of rooms)
have some impact.
Lasso Regression – Shrinks less important features to zero
Eg: In medical diagnosis, Lasso can select only the most relevant
symptoms while ignoring minor ones.

Classification Algorithms (For Predicting
Categories)
Logistic Regression – Classifies data into two or more categories.
Eg: Predicts if an email is spam or not.
Linear Discriminant Analysis (LDA)– LDA is mainly used when there are multiple categories
to classify efficiently
Eg: Credit Card Fraud Detection – Identifies fraudulent transactions by distinguishing normal
vs. suspicious behavior.
Naïve Bayes : A probability-based classifier that assumes features are independent.

Classification Algorithms (For Predicting Categories)
Perceptron Algorithm : A simple model that learns to classify data into two groups using a
straight-line decision boundary.
✅ Handwritten Digit Recognition – Identifies if a digit is "0" or "1".
✅ Spam Detection – Decides if an email is spam or not.
✅ Disease Prediction – Classifies if a person has a disease or not.

Perceptron Algorithm : The Perceptron Learning Algorithm is a binary classification
algorithm that adjusts weights associated with input features iteratively based on
misclassifications, aiming to find a decision boundary that separates classes.

Perceptron Algorithm : Perceptron is a type of neural network that performs binary
classification that maps input features to an output decision, usually classifying data into
one of two categories, such as 0 or 1.

Regression and Classification Algorithms

Over-Fitting – Under-Fitting

Linear Regression Models
 Linear Regression is a supervised learning algorithm used to predict continuous values
based on input features.
 A linear regression model is a statistical method used to predict a dependent variable (Y)
based on one or more independent variables (X).
 It assumes a linear relationship between the variables and fits a straight line to minimize
errors.
X-axis → Represents the independent variable (e.g., study hours).
Y-axis → Represents the dependent variable (e.g., exam scores).
Line of Best Fit → A straight line that best represents the data trend

Simple Example
Imagine you own a car dealership and want to predict the price of a car based on its age.
Independent Variable (Input): Age of the car (in years)
Dependent Variable (Output): Price of the car (in dollars)
• When you plot this data on a graph, older cars
tend to have lower prices, forming a downward
trend.
• Linear regression finds the best-fit straight line
that represents this trend.

Simple Example
Imagine you own a car dealership and want to predict the price of a car based on its age.
Independent Variable (Input): Age of the car (in years)
Dependent Variable (Output): Price of the car (in dollars)

Types of Linear Regression Models
1. Simple Linear Regression
Definition: Uses one independent variable to predict the outcome.
Example: Predicting car price based on age.
2. Multiple Linear Regression
Definition: Uses multiple independent variables to predict the outcome.
Example: Predicting house price based on square footage, number of bedrooms, and location.

Types of Linear Regression Models

• Both Simple Linear Regression and Multiple Linear Regression use the
Least Squares Method to find the best-fit line by minimizing the error
(difference between actual and predicted values).
• Least Squares Method is a mathematical technique used to find the best-
fitting line or curve by minimizing the sum of squared differences between
actual and predicted values.
• It is widely used in regression analysis, data science, and AI to make accurate
predictions from given data

Least Squares Method - Finding The “Line Of Best Fit”

Least Squares Method

.
Libraries for Single Linear Regression, Multiple Linear Regression, and Least Squares Calculation:

.
Python code for Single Linear Regression, Multiple Linear Regression, and Least Squares Calculation:

.
Python code for Single Linear Regression, Multiple Linear Regression, and Least Squares Calculation:
print(f"Equation: Price = {model.intercept_:.2f} + {model.coef_[0]:.4f}(Size) + {model.coef_[1]:.2f}(Bedrooms)")

.
• Bayesian Linear Regression and Gradient Descent play different roles in building a linear regression model:
• Both use different techniques to find the best-fit line.
Bayesian Linear Regression:
 Think of it like making predictions with confidence.
 Instead of finding just one best-fit line, it gives a range of possible lines based on prior knowledge and new data.
 It helps in understanding uncertainty in predictions.

.
Bayesian Linear Regression:
 Think of it like making predictions with confidence.
 Instead of finding just one best-fit line, it gives a range of possible lines based on prior knowledge and new data.
 It helps in understanding uncertainty in predictions.

.
Bayesian Linear Regression: Bayesian theorem

.
Gradient Descent – An Optimization Algorithm
 A gradient is like a direction and speed guide that tells us how to change something to get a better result.
 "Descent" simply means moving downward or going lower.
 Gradient Descent is an optimization algorithm used to minimize a cost function by iteratively adjusting parameters.
 It helps machine learning models learn by updating weights in the direction that reduces error.
 Commonly used in linear regression, logistic regression, and neural networks.

.
Gradient Descent – An Optimization Algorithm

Linear Classification Models
 A linear classification model is a machine learning model that separates data into categories using a straight line (or
a plane in higher dimensions).
 It classifies data points by drawing a boundary (called a decision boundary) based on a linear equation.

DISCRIMINANT FUNCTION
 A discriminant function is a statistical tool that classifies data
into groups
 It's used in pattern classifiers, medical research, and other
field
 A discriminant function in machine learning is a
function that separates data points into different
classes.
 It's used in pattern classifiers to determine the class of
an input.

PERCEPTRON ALGORITHM
 The Perceptron Algorithm is the simplest type of artificial
neural network used for binary classification (deciding
between two categories like "Yes/No" or "Spam/Not Spam").
 It works by adjusting weights to find the best decision
boundary that separates two classes.

PERCEPTRON ALGORITHM

PROBABILISTIC DISCRIMINATIVE MODEL and
 A Probabilistic Discriminative Model - Finds the boundary between classes and directly
predicts the probability of a class given an input.
 Example: Spam Filter – Predicts how likely an email is spam (e.g., 80% Spam, 20% Not Spam).
 Probabilistic Generative Model → Learns how data is generated for each class and then uses
that to classify new data.
 Example: Face Generator – Learns the features of human faces and can generate new faces
based on the learned patterns.

Logistic Regression
 Logistic Regression is a machine learning algorithm used for classification problems.
 It predicts whether something belongs to one category or another (e.g., Yes/No, Spam/Not Spam).
 Instead of giving a direct "Yes" or "No," it gives a probability and applies a threshold (usually 0.5).

UNIT II SUPERVISED LEARNING - Introduction

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