22PCOAM16 Unit 3 Session 23 Different ways to Combine Classifiers. pptx

1. 14/05/2025 1
Department of Computer Science & Engineering (SB-ET)
III B. Tech -I Semester
MACHINE LEARNING
SUBJECT CODE: 22PCOAM16
AcademicY
ear: 2024-2025
by
Dr. M.Gokilavani
GNITC
Department of CSE (SB-ET)

2. 14/05/2025 Department of CSE (SB-ET) 2
22PCOAM16 MACHINE LEARNING
UNIT – III
Syllabus
Learning with Trees – Decision Trees – Constructing Decision Trees –
Classification and Regression Trees – Ensemble Learning – Boosting –
Bagging – Different ways to Combine Classifiers – Basic Statistics –
Gaussian Mixture Models – Nearest Neighbor Methods – Unsupervised
Learning – K means Algorithms

3. 14/05/2025 3
TEXTBOOK:
• Stephen Marsland, Machine Learning - An Algorithmic Perspective, Second Edition,
Chapman and Hall/CRC.
• Machine Learning and Pattern Recognition Series, 2014.
REFERENCES:
• Tom M Mitchell, Machine Learning, First Edition, McGraw Hill Education, 2013.
• Ethem Alpaydin, Introduction to Machine Learning 3e (Adaptive Computation and
Machine
No of Hours Required: 13
Department of CSE (SB-ET)
UNIT - III LECTURE - 23

4. 14/05/2025 Department of CSE (SB-ET) 4
Need of Classifier
• A classifier in machine learning is an algorithm that automatically orders or
categorizes data into one or more of a set of “classes.”
• The process of categorizing or classifying information based on certain
characteristics is known as classification.
• Classifiers are typically used in supervised learning systems where the
correct class for each input example is known during training.
• The goal of a classifier is to learn from the training data and be able to
make accurate predictions on unseen data.
UNIT - III LECTURE - 23

5. 14/05/2025 Department of CSE (SB-ET) 5
Types of Classifiers
• Binary Classifiers: These are used when there are only two possible
classes. For example, an email classifier might be designed to detect spam
and non-spam emails.
• Multiclass Classifiers: These handle situations where there are more than
two classes. For example, a classifier that categorizes news articles into
'sports', 'politics', 'technology', etc.
• Multilabel Classifiers: These can assign multiple labels to each instance.
For example, a movie could be classified into multiple genres like
'comedy', 'drama', and 'action' simultaneously.
UNIT - III LECTURE - 23

6. 14/05/2025 Department of CSE (SB-ET) 6
How Does a Classifier Work?
• A classifier works by learning the relationship between input features and
the class labels in the training data, and then applying this learned
relationship to predict the class of new examples. This process involves the
following steps:
• Data Preprocessing: Input data is cleaned and transformed into a format
that can be fed into a machine learning model.
• Feature Selection: The most informative features are selected to train the
classifier.
UNIT - III LECTURE - 23

7. 14/05/2025 Department of CSE (SB-ET) 7
How Does a Classifier Work?
• Model Training: The classifier algorithm learns from the training data by
adjusting its parameters to minimize a loss function.
• Model Evaluation: The classifier's performance is assessed using metrics
such as accuracy, precision, recall, and F1-score.
• Prediction: The trained classifier is used to predict the class labels of new,
unseen data.
UNIT - III LECTURE - 23

8. 14/05/2025 Department of CSE (SB-ET) 8
Classification Algorithms
• Implementation of any classification model it is essential to
understand Logistic Regression, which is one of the most fundamental and
widely used algorithms in machine learning for classification tasks.
• There are various types of classifiers algorithms.
• Linear classifier
• Non Linear classifier
UNIT - III LECTURE - 23

9. 14/05/2025 Department of CSE (SB-ET) 9
Linear Classifiers
• Linear Classifiers: Linear classifier models create a linear decision
boundary between classes.
• They are simple and computationally efficient. Some of the
linear classification models are as follows:
• Logistic Regression
• Support Vector Machines having kernel = ‘linear’
• Single layer Perception
• Stochastic Gradient Descent (SGD) classifier
UNIT - III LECTURE - 23

10. 14/05/2025 Department of CSE (SB-ET) 10
Non-linear Classifiers
• Non-linear Classifiers: Non-linear models create a non-linear decision boundary
between classes.
• They can capture more complex relationships between input features and target
variable. Some of the non-linear classification models are as follows:
• K-Nearest Neighbors Kernel SVM
• Naive Bayes
• Decision Tree Classification
• Ensemble learning classifiers
• Random Forests
• AdaBoost
• Bagging Classifier
UNIT - III LECTURE - 23

11. 14/05/2025 Department of CSE (SB-ET) 11
Topics to be covered in next session 24
• Unit III
Thank you!!!
UNIT - III LECTURE - 23