This document discusses supervised learning algorithms. It defines supervised learning as using labeled datasets to train algorithms to classify data or predict outcomes accurately. Some commonly used supervised learning algorithms are discussed, including neural networks, naive Bayes, linear regression, logistic regression, support vector machines, k-nearest neighbors, and random forests. These algorithms are used to build models that can generate predictions for new data based on patterns learned from training data.

1. P1WU
UNIT – III: CLASSIFICATION
Topic 4: SUPERVISED ALGORITHMS
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2. UNIT III : TEXT CLASSIFICATION AND CLUSTERING
1.A Characterization of Text
Classification
2. Unsupervised Algorithms:
Clustering
3. Naïve Text Classification
4. Supervised Algorithms
5. Decision Tree
6. k-NN Classifier
7. SVM Classifier
8. Feature Selection or
Dimensionality Reduction
9. Evaluation metrics
10. Accuracy and Error
11. Organizing the classes
12. Indexing and Searching
13. Inverted Indexes
14. Sequential Searching
15. Multi-dimensional
Indexing
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3. SUPERVIZED LEARNING
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4. INTRODUCTION TO SUPERVIZED LEARNING
• Supervised learning, also
known as supervised machine
learning, is a subcategory of
machine learning and artificial
intelligence.
• It is defined by its use of
labeled datasets to train
algorithms that to classify data
or predict outcomes accurately.
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5. What is supervised learning?
• Supervised learning, also known as supervised machine
learning, is
• a subcategory of machine learning and artificial intelligence.
• It is defined by
• its use of labeled datasets to train algorithms that to classify
data or predict outcomes accurately.
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6. Supervised Learning
• It is defined by its use of labeled datasets to
• train algorithms that to classify data or predict outcomes accurately.
• As input data is fed into the model, it adjusts its weights until the
model has been fitted appropriately,
• which occurs as part of the cross validation process.
• Supervised learning helps organizations solve for
• a variety of real-world problems at scale, such as classifying spam in a
separate folder from your inbox.
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7. What is the type of supervised learning?
• There are two types of Supervised Learning
techniques:
1. Regression and
2. Classification.
• Classification separates the data, Regression fits the
data.
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8. Example of Supervised Learning
• A great example of supervised learning is text classification
problems.
• In this set of problems, the goal is to
• predict the class label of a given piece of text.
• One particularly popular topic in text classification is to
• predict the sentiment of a piece of text, like a tweet or a product
review.
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9. SUPERVIZED ALGORITHMS
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10. INTRODUCTION TO SUPERVIZED ALGORITHMS
• Which are supervised algorithm?
• A supervised learning algorithm takes
• a known set of input data (the learning set) and known responses to the data
(the output), and forms a model to generate reasonable predictions for the
response to the new input data.
• Use supervised learning if you have existing data for the output
you are trying to predict.
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11. SUPERVIZED ALGORITHMS EXAMPLE
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12. INTRODUCTION TO SUPERVIZED ALGORITHMS
• Various algorithms and computation techniques are used in
supervised machine learning processes.
• Most commonly used learning methods, typically calculated
through use of programs like R or Python are:
1) Neural networks
• Primarily leveraged for deep learning algorithms, neural networks process
training data by mimicking the interconnectivity of the human brain through
layers of nodes
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13. INTRODUCTION TO SUPERVIZED ALGORITHMS
2) Naive Bayes
• Naive Bayes is classification approach that adopts the principle of class conditional
independence from the Bayes Theorem.
3) Linear regression
• Linear regression is used to identify the relationship between a dependent variable and
one or more independent variables and is typically leveraged to make predictions about
future outcomes.
4) Logistic regression
• While linear regression is leveraged when dependent variables are continuous, logistical
regression is selected when the dependent variable is categorical, meaning they have
binary outputs, such as "true" and "false" or "yes" and "no."
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14. INTRODUCTION TO SUPERVIZED ALGORITHMS
5) Support vector machine (SVM)
• A support vector machine is a popular supervised learning model developed by
Vladimir Vapnik, used for both data classification and regression.
6) K-nearest neighbor
• K-nearest neighbor, also known as the KNN algorithm, is a non-parametric
algorithm that classifies data points based on their proximity and association to
other available data.
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15. INTRODUCTION TO SUPERVIZED ALGORITHMS
7) Random forest
• Random forest is another flexible supervised machine learning algorithm used
for both classification and regression purposes.
• The "forest" references a collection of uncorrelated decision trees, which are
then merged together to reduce variance and create more accurate data
predictions.
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16. Any Questions?
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