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/finance-
accounting/difference-between-supervised-and-
unsupervised-learning
•
Email:
support@mindcypress.com
Phone: +
1-206-922-2417
+971 50 142 7401
If you want to learn more about supervised and unsupervised learning, you should enroll in a financial modeling training course online.
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Presentation Transcript
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/finance- accounting/difference-between-supervised-and- unsupervised-learning Email: support@mindcypress.com Phone: +1-206-922-2417 +971 50 142 7401
