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Classification vs. Regression Models

Data Science, Risk Management

This lesson is part 2 of 28 in the course Credit Risk Modelling in R

While building any predictive model, it is important to first understand whether it is a classification or a regression problem. Let’s understand the difference between the two:

1. Classification

In a classification problem, we are trying to predict the class of a data point (discreet number of values). The Y variable that we are trying to predict generally comes in categorical form and has a finite number of classes. For example, we can classify a loan as Default or No Default. Or we can classify an image as a cat or a dog. The credit risk problem that we are trying to solve is a classification problem. We call it a binary classification when there are only one of the two classes to predict (Default or No Default – 0 or 1). If we have more than 2 classes, we call it a multi-classification problem. Such models are commonly referred to as “classifiers”.

2. Regression

The problem we are solving is considered a regression problem if we are predicting a continuous valued output, for example, predicting the price of a house, or stock prices.

When we are solving a data science problem, we will first define our problem as a classification or a regression problem, depending on the output that we are trying to predict.

In our case, we can conclude that predicting default is a classification problem. Let’s now start with our first case study and understand the steps involved in model building.

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In this Course

  • Credit Risk Modelling – Case Studies
  • Classification vs. Regression Models
  • Case Study – German Credit – Steps to Build a Predictive Model
  • Import Credit Data Set in R
  • German Credit Data : Data Preprocessing and Feature Selection in R
  • Credit Modelling: Training and Test Data Sets
  • Build the Predictive Model
  • Logistic Regression Model in R
  • Measure Model Performance in R Using ROCR Package
  • Create a Confusion Matrix in R
  • Credit Risk Modelling – Case Study- Lending Club Data
  • Explore Loan Data in R – Loan Grade and Interest Rate
  • Credit Risk Modelling – Required R Packages
  • Loan Data – Training and Test Data Sets
  • Data Cleaning in R – Part 1
  • Data Cleaning in R – Part 2
  • Data Cleaning in R – Part 3
  • Data Cleaning in R – Part 5
  • Remove Dimensions By Fitting Logistic Regression
  • Create a Function and Prepare Test Data in R
  • Building Credit Risk Model
  • Credit Risk – Logistic Regression Model in R
  • Support Vector Machine (SVM) Model in R
  • Random Forest Model in R
  • Extreme Gradient Boosting in R
  • Predictive Modelling: Averaging Results from Multiple Models
  • Predictive Modelling: Comparing Model Results
  • How Insurance Companies Calculate Risk

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    • Financial Reporting Standards
    • Fraud
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