Difference Between Supervised and Unsupervised Learning
                     Difference Between 
Supervised and 
Unsupervised Learning
Introduction
It is important to know the difference between 
supervised and unsupervised learning when 
you’re receiving your financial modeling 
certification. 
Depending on the type of situation at hand, 
these two crucial approaches—which serve 
different purposes—are utilized to evaluate 
and extract insights from data.

Supervised Learning
Training a model on labeled data with specified 
input data (features) and corresponding output 
(labels or goal variable) is known as supervised 
learning. You will learn more about it thoroughly 
during your financial modeling training course 
online. To accurately forecast the output for 
fresh, unseen data, the model must learn the 
mapping function from the input to the output.
Key Characteristics:
• Labeled Data: Examples of both the input 
and the intended output are included in the 
training dataset.
• Training Process: By modifying its 
parameters to reduce the error between 
expected and actual outputs, the model 
learns from the labeled data.
• Types of Tasks: Regression (predicting 
continuous variables) and classification 
(predicting categories) are frequent tasks.
• Examples: Spam email identification, 
feature-based housing price prediction, and 
picture classification (e.g., object recognition 
in photographs).
Advantages and Disadvantages
Advantages:
• Clearly defined goal with well-known output 
labels.
• Capacity to use labeled test data to quantify 
and validate model performance.
Disadvantages:
• Needs a lot of labeled data in order to be 
trained.
• If there are flaws or noise in the labeled data, 
it might not function properly.
Unsupervised Learning
In unsupervised learning, a model is trained on unlabeled data, and 
instead of having a specific output variable to predict, the program 
looks for patterns or hidden structures in the input data. The 
objective is to examine the data and identify underlying patterns or 
clusters that can shed light on the underlying structure of the data. 
You will learn more about the same during your financial modeling 
training course online.
Key Characteristics:
• Unlabeled Data: There are no target variables or predetermined 
output labels in the training dataset.
• Training Process: By comparing and contrasting data points, the 
model finds patterns or clusters in the data.
• Types of Tasks: Typical tasks include association (determining 
connections between variables), anomaly detection (spotting odd 
patterns), and clustering (assembling comparable data points).
• Examples: Examples include market basket analysis (e.g., product 
recommendations based on purchasing history), customer 
segmentation, and fraud detection.
Advantages and Disadvantages
Advantages:
• May reveal hidden structures and patterns in 
data.
• Beneficial for comprehending data linkages 
and conducting exploratory data analysis.
Disadvantages:
• Since there is no labeled data, there are no 
objective evaluation metrics available.
• Results interpretation can be arbitrary and 
call for subject-matter expertise.
Key Differences Summarized
• Data Type: Labeled data is used in supervised 
learning, whereas unlabeled data is used in 
unsupervised learning.
• Objective: The goal of unsupervised learning is to 
find hidden patterns or groups, whereas the goal of 
supervised learning is to predict output labels or 
values.
• Evaluation: While the assessment of 
unsupervised learning models is more arbitrary 
and context-dependent, that of supervised 
learning models may be done objectively using 
metrics like accuracy or mean squared error.
In conclusion, the decision between supervised and unsupervised learning is based on the particular problem that needs 
to be handled as well as the characteristics of the data. While unsupervised learning is useful for investigating and 
comprehending complicated data structures without predetermined results, supervised learning is appropriate when 
there is a clear objective with labeled data. These approaches are essential to machine learning applications, advancing a 
number of industries including marketing, finance, and healthcare.
If you want to learn more about supervised and unsupervised learning, you should enroll in a 
financial modeling training course online.
Slide End & Resource
• Resource: 
https://www.mindcypress.com/blogs/fina
nce-accounting/difference-between-supe
rvised-and-unsupervised-learning
 
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