This document discusses various classification algorithms used in business intelligence: K-nearest neighbors, decision trees, naive Bayes classifier, logistic regression, artificial neural networks, and support vector machines. It provides more detail on logistic regression, describing it as a predictive analysis technique used to predict binary outcomes based on independent variables. It compares logistic regression to linear regression. The document also provides overviews of artificial neural networks, describing their representation of neurons and neural connections, and support vector machines, explaining how they select hyperplanes to best separate classified data points.

1. OMega TechEd
Part-II
10

2. BUSINESS INTELLIGENCE
CLASSIFICATION ALGORITHMS
Part-II
Mrs. Megha Sharma
M.Sc. Computer Science, B.Ed.

3. K-Nearest Neighbours
Decision trees
Naive Baye’s Classifier
Logistic Regression
Artificial Neural Network
Support Vector Machines
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4. 4. Logistic Regression
Logistic regression is a predictive analysis technique. It is used to
describe data and to explain the relationship between one
dependent binary variable and one or more nominal, ordinal
interval or ratio-level independent variable.
 Logistic regression predicts the probability of an outcome that can
only have two values (Yes or No)
E.g. probability of getting attending college (YES or NO)
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5. Linear Regression vs Logistic Regression
Linear Regression: Used to predict the
continuous dependent variable using a
given set of independent variables.
Logistic regression: Used to predict the
categorical dependent variable using a given
set of independent variables.
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6. Neural Network.
 Neurons are vital part of human brain which does simple
input/output to complex problem solving in the brain.
Neural network is a network of nerve cells in the brain. There
are about 100 millions neurons in our brain.
Dendrites extend from the neuron cell body and receive
messages from other neurons. Synapses are the contact points
where one neuron communicates with another.
 The data transfer is achieved by the exchange of electrical or
chemical signal with the help of synapses.
They exchange about 1000 trillion synaptic signal per second.
 A human brain can store upto 1000 terabytes of data.
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7. 5. Artificial Neural Networks.
 Artificial Neural Networks (ANN) is an artificial
representation of a human brain that tries to simulate its
various functions such as learning, calculating,
understanding, decision making and many more.
 The neuron is actually a processing unit, it calculates
the weighted sum of the input signal to the neuron to
generate the activation signal a, given by:
 a= Ʃ wi xi i=1 to N
 Threshold is defined as THETHA in neural network
model. It is added or subtracted to the output depending
upon the model definitions.
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8. 6. Support Vector Machine
 Support vector machine is an algorithm which is useful for classification in a
supervised learning algorithm example. It does classification of the inputs
received on the basis of the rule-set. It also works on regression problems.
 In Support vector machine , a hyperplane is selected to best separate the
points in the input variable space by their class.
margin The distance between the support
vectors and the hyperplanes are as
far as possible.
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9. Thanks For Watching.
Next Topic : Clustering.

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