Supervised learning and unsupervised learning new 2024.pdf

Machin_learnning

1. Supervised learning and unsupervised
learning
Supervised learning and unsupervised
learning are two main types of machine
learning, each with distinct characteristics
and use cases. Here's a breakdown of their
differences:

2. Data Labeling
•Supervised Learning: Uses labeled data, meaning each
training example is paired with an output label. For
example, in image classification, each image (input) has
a corresponding category (output label).
•Unsupervised Learning: Uses unlabeled data, meaning
the algorithm must find patterns and relationships on
its own, without any specific guidance on what to look
for.

3. Goal
•Supervised Learning: The goal is to learn a mapping
function from input variables to output variables,
enabling the model to predict outputs for new inputs.
It's like learning with a teacher.
•Unsupervised Learning: The goal is to discover hidden
patterns or intrinsic structures in the data. It's like
learning without a teacher.

4. Types of Problems
•Supervised Learning: Typical tasks include
classification (e.g., spam detection) and
regression (e.g., predicting house prices).
•Unsupervised Learning: Typical tasks include
clustering (e.g., customer segmentation),
dimensionality reduction (e.g., Principal
Component Analysis), and association (e.g.,
market basket analysis).

5. Evaluation
1.Supervised Learning: Model performance can
be easily evaluated using the labeled data
(e.g., accuracy, precision, recall, F1 score).
2.Unsupervised Learning: Evaluation is more
challenging due to the absence of ground
truth labels. Often, domain experts must
manually evaluate the results.

6. Examples of Algorithms
•Supervised Learning: Linear Regression, Logistic
Regression, Decision Trees, Random Forests,
Support Vector Machines (SVM), and Naive Bayes.
•Unsupervised Learning: K-Means Clustering,
Hierarchical Clustering, DBSCAN, Principal
Component Analysis (PCA), and Autoencoders.