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Module 3 - Classification.md

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Module Introduction

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

Learning Objectives

  • To understand different Classification methods.
  • To apply Classification algorithms on various data sets to solve real world problems.
  • To understand evaluation methods in Classification.

K-Nearest-neighbors

K-Nearest-neighbors

Decision Trees

Decision Trees

Logistic Regression

Logistic Regression

SVM (Support Vector Machines)

SVM (Support Vector Machines)

Model 3 - Graded Quiz

Question 1: Which one IS NOT a sample of a classification problem?

  • A. [ ] To predict whether a customer responds to a particular advertising campaign or not.
  • B. [X] To predict the amount of money a customer will spend in one year.
  • C. [ ] To predict whether a customer switches to another provider/brand.
  • D. [ ] To predict the category to which a customer belongs.

Question 2: Which of the following statements is TRUE about Logistic Regression?

  • A. [ ] In logistic regression, the dependent variable is binary.
  • B. [ ] Logistic regression is analogous to linear regression but takes a categorical/discrete target field instead of a numeric one.
  • C. [ ] Logistic regression can be used both for binary classification and multi-class classification.
  • D. [X] All of the above.

Question 3: Which of the following examples is/are a sample application of Logistic Regression? (Select all that apply)

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

Question 4: Which one is TRUE about the kNN algorithm?

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

Question 5: What is "information gain" in decision trees?

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