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What type of variable is used in predicting whether a person will buy a new insurance product?
Multiple linear regression is used when predicting a categorical dependent variable.
False
What is the primary approach used by Experian to predict customer conversion?
Logistic Regression
In multiple linear regression, multiple independent variables are used to predict a ________ dependent variable.
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Match the following projects with their respective predictions:
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Which of the following is NOT a project conducted by Experian?
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Experian uses historical data to train its multiple linear regression models.
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What does the abbreviation MLR stand for?
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What is a characteristic of a parametric model?
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Optimization involves defining a function that represents 'error.'
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What algorithm is often used for parameter optimization in models?
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The objective function in MLR minimizes the sum of ______ errors.
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Match the following types of models with their characteristics:
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Which of the following examples illustrates a non-parametric model?
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A non-parametric model has no parameters at all.
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What is the main purpose of testing an optimized model?
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What are the two fundamental analytics models covered in this week?
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Multiple Linear Regression is primarily used for classification tasks.
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Which project focuses on predicting the number of customers for a store location?
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Regression problems always involve predicting categorical variables.
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What is a hyperplane in the context of regression and classification models?
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Experian operates in _____ countries and has approximately _____ employees.
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What kind of regression is used when there is more than one input variable?
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Match the following aspects of Experian with their respective project focus:
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The ________ variable is being predicted when assessing the likelihood of a customer's default.
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Which application is likely to be focused on market intelligence?
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Match the following regression projects with their predictions:
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What is the main goal of the Northern Power Grid project?
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Optimization is unimportant in business analytics.
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What is the significance of 'Parametric Modelling' in analytics?
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The mean and median are both measures used in regression analysis.
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List one input feature used in customer numbers modeling.
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What is the primary purpose of performing an F-test in regression analysis?
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Linear regression is suitable for predicting categorical outcomes.
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What type of model is often preferred when predicting classes in business analytics?
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The outcome categories for classification problems can include _____ or _____ predictions.
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Match the following classification terms with their corresponding categories:
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Why might linear regression produce nonsensical results in certain situations?
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Logistic regression is utilized to model relationships in probability space.
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Provide an example of a classification problem.
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Study Notes
The Story So Far
- Supervised learning and unsupervised learning are the two main types of machine learning
- Classification and clustering are both discrete types of supervised learning
- Regression is a continuous type of supervised learning
- Point models and linear regression are two examples of regression models
- Dimensionality reduction and regression are both components of unsupervised learning
Today’s Learning Objectives
- The two fundamental analytics models covered: Multiple Linear Regression (MLR) and Multiple Logistic Regression.
- Understanding the equations behind both models.
- Comprehending the differences between regression and classification techniques and what a hyperplane is.
- Understanding the concept of “Parametric Modelling” and its importance in business analytics.
- Recognizing why “optimization” is crucial to successful business analytics.
Experian
- A leading user of modern business analytics
- They use analytics for internal processes and externally by selling analytics services and products in 80 countries.
- They have 18,000 employees in 39 countries with a revenue of approximately £4.7 billion.
- Experian provides examples of specific projects in industries such as credit risk, market intelligence, consumer insight, and vehicle insight.
Experian Projects
- Opticians Store Locations: This project aims to predict the likely number of customers to a specific store location.
- Credit Rating Prediction: This project aims to predict total losses to the business if customers default.
- Northern Power Grid: This aims to predict the vulnerability of customers in certain locations to power outages.
Customer “Numbers” Modelling:
- Experian uses more than one input variable to make predictions.
- When your linear regression problem has more than one input variable and one output, you're doing multiple linear regression.
Customer “Conversion” Modelling
- Experian uses Logistic Regression to predict whether potential customers will convert based on direct mail campaigns.
Multiple Linear Regression (MLR)
- MLR is used when predicting a continuous dependent variable (output) from more than one independent variable (input).
- In MLR, the relationship between variables is visualized with the help of planes instead of simple straight lines.
- MLR models are trained by tuning parameters so the model's predictions minimize error when tested on historical data.
“Parametric” Models
- Parametric models use equations with parameters that must be trained.
- Optimization uses an "optimizer" or "solver" to find the best parameters for the model.
Optimizing Parameters
- To find the best parameters, an objective function (generally an equation that represents error) is defined.
- The optimizer/solver receives instructions on which parameters to adjust.
- The optimization process continues until it finds a parameterization with minimal identified error or until time or patience runs out.
Optimizing Parameters in “MLR”
- The objective function for MLR is the total error the model produces when tested on historical data.
- This objective function traditionally minimizes the sum of squared errors (residuals) or the difference between the model's prediction and the actual value for each data point.
Testing an Optimized Model
- To determine the trustworthiness of the model, formal testing procedures are used.
- Various parametric tests assign a p-value to each coefficient in a regression, which indicates significance and reliability.
- The model that makes the best predictions is usually chosen.
Classification Problems
- Classification problems involve predicting a class or category when a continuous variable isn't being predicted.
- Examples of classification problems include predicting purchase/no-purchase, defaulter/non-defaulter, churner/non-churner, and converter/non-converter.
- Linear regression isn't suitable for classification problems as it's designed for predicting continuous variables.
Classification Business Problems
- Classifiers are more common than regressors in business analytics.
- Examples of classification problems in various fields include: malignant/benign in public analytics, breach/non-breach in consumer analytics, upturn/downturn in financial analytics, win/loss/draw in sports analytics, outbreak/no outbreak in public health analytics, lapser/non-lapser in customer retention, give credit/reject in credit scoring, and buy/sell in stock trading.
Linear Regression and Classification Problems
- Linear regression isn't suitable for classification problems because it cannot give accurate classifications when predicting categorical variables.
The Solution: Logistic Regression
- Logistic Regression solves classification problems by using a different line for the model and a probabilistic space.
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Description
This quiz covers the fundamentals of business analytics with a focus on Multiple Linear Regression (MLR) and Multiple Logistic Regression. You will explore the equations behind these models, the differences between regression and classification techniques, and the significance of parametric modeling in business analytics. Additionally, the role of optimization in achieving successful outcomes will be highlighted.