In this module, you will learn about classification technique. You practice with different classification algorithms, such as KNN, Decision Trees, Logistic Regression and SVM. Also, you learn about pros and cons of each method, and different classification accuracy metrics.

Learning Objectives

• Compare and contrast the characteristics of different Classification methods.
• Explain how to apply the K Nearest Neighbors algorithm.
• Describe model evaluation metrics.
• Describe how a decision tree works.
• Outline how to build a decision tree.
• Explain the capabilities of logistic regression.
• Compare and contrast linear regression with logistic regression.
• Explain how to change the parameters of a logistic regression model.
• Describe the cost function and gradient descent in logistic regression.
• Provide an overview of the Support Vector Machine method.
• Apply Classification algorithms on various datasets to solve real world problems.

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• K-Nearest Neighbors

  • Lab 3-1: KNN

• Decision Trees

  • Lab 3-2: Decision Trees

• Logistic Regression

  • Lab 3-3: Logistic Regression

• Support Vector Machine

  • Lab 3-4: SVM (Support Vector Machines)

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Quiz: Classification

TOTAL POINTS 9

Question 1

Which of the following examples is/are a sample application of Logistic Regression? (select all that apply)

• The probability that a person has a heart attack within a specified time period using person's age and sex.
• Customer's propensity to purchase a product or halt a subscription in marketing applications.
• Likelihood of a homeowner defaulting on a mortgage.
• Estimating the blood pressure of a patient based on her symptoms and biographical data.

Correct

Question 2

Which one is TRUE about the kNN algorithm?

• kNN is a classification algorithm that takes a bunch of unlabelled points and uses them to learn how to label other points.
• kNN algorithm can be used to estimate values for a continuous target.

Correct

Question 3

What is "information gain" in decision trees?

• It is the information that can decrease the level of certainty after splitting in each node.
• It is the entropy of a tree before split minus weighted entropy after split by an attribute.
• It is the amount of information disorder, or the amount of randomness in each node.

Correct