Classification Algorithm-II
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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.
Classification Algorithm-II
- 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 Subscribe
- 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) Subscribe
- 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. Size Weight Overweight 1 Not overweight 0 Subscribe
- 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. Subscribe
- 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. Subscribe
- 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. Subscribe
- 9. Thanks For Watching. Next Topic : Clustering.
- 10. About the Channel This channel helps you to prepare for BSc IT and BSc computer science subjects. In this channel we will learn Business Intelligence , A.I., Digital Electronics, Internet OF Things Python programming , Data-Structure etc. Which is useful for upcoming university exams. Gmail: [email protected] Social Media Handles: omega.teched megha_with OMega TechEd