Difference Between Supervised and Unsupervised Learning

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Difference Between Supervised and Unsupervised
Learning
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Introduction
It is important to know the difference between
supervised and unsupervised learning when youre
receiving your financial modeling certification.
Depending on the type of situation at hand,
these two crucial approacheswhich serve
different purposesare utilized to evaluate and
extract insights from data.
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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.
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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).

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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.

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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.

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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.

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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.
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Slide End Resource

• Resource https//www.mindcypress.com/blogs/fina
  nce-accounting/difference-between-supervised-and-u
  nsupervised-learning
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