Question 1

A test set is the data set used to

Accepted Answer

A) build the data mining model. 
B) estimate performance of candidate models on unseen data. 
C) estimate performance of the final model on unseen data. 
D) show counts of actual versus predicted class values. 
A) build the data mining model. 
B) estimate performance of candidate models on unseen data. 
C) estimate performance of the final model on unseen data. 
D) show counts of actual versus predicted class values.

Question 2

A(n) __________ is often displayed as a row of values in a spreadsheet or database in which the columns correspond to the variables.

Accepted Answer

A) record 
B) data point 
C) classification 
D) location 
A) record 
B) data point 
C) classification 
D) location

Question 3

__________ involves descriptive statistics, data visualization, and clustering.

Accepted Answer

A) Data exploration 
B) Data partitioning 
C) Data preparation 
D) Model assessment 
A) Data exploration 
B) Data partitioning 
C) Data preparation 
D) Model assessment

Question 4

The y-axis of a decile chart shows

Accepted Answer

A) number of important class records identified. 
B) ratio of decile mean to overall mean. 
C) the number of actual Class 1 records. 
D) the ratio of the overall mean to the decile mean. 
A) number of important class records identified. 
B) ratio of decile mean to overall mean. 
C) the number of actual Class 1 records. 
D) the ratio of the overall mean to the decile mean.

Question 5

__________ is a generalization of linear regression for predicting a categorical outcome variable.

Accepted Answer

A) Multiple linear regression 
B) Logistic regression 
C) Discriminant analysis 
D) Cluster analysis 
A) Multiple linear regression 
B) Logistic regression 
C) Discriminant analysis 
D) Cluster analysis

Question 6

A __________ classifies a categorical outcome variable by splitting observations into groups via a sequence of hierarchical rules.

Accepted Answer

A) regression tree 
B) scatter chart 
C) classification tree 
D) confusion matrix 
A) regression tree 
B) scatter chart 
C) classification tree 
D) confusion matrix

Question 7

__________ is the manipulation of the data with the goal of putting it in a form suitable for formal modeling.

Accepted Answer

A) Data sampling 
B) Data partitioning 
C) Data preparation 
D) Model assessment 
A) Data sampling 
B) Data partitioning 
C) Data preparation 
D) Model assessment

Question 8

A characteristic or quantity of interest that can take on different values is a(n)

Accepted Answer

A) variable. 
B) observation. 
C) record. 
D) quality. 
A) variable. 
B) observation. 
C) record. 
D) quality.

Question 9

Estimation methods are also referred to as

Accepted Answer

A) prediction methods. 
B) clustering methods. 
C) association methods. 
D) supervised methods. 
A) prediction methods. 
B) clustering methods. 
C) association methods. 
D) supervised methods.

Question 10

__________ is a measure of the heterogeneity of observations in a classification tree.

Accepted Answer

A) Sensitivity 
B) Specificity 
C) Accuracy 
D) Impurity 
A) Sensitivity 
B) Specificity 
C) Accuracy 
D) Impurity

Question 11

The x-axis of a lift chart shows

Accepted Answer

A) the number of actual Class 1 records identified. 
B) the ratio of decile mean to overall mean. 
C) the number of actual Class 1 records. 
D) the ratio of the overall mean to the decile mean. 
A) the number of actual Class 1 records identified. 
B) the ratio of decile mean to overall mean. 
C) the number of actual Class 1 records. 
D) the ratio of the overall mean to the decile mean.

Question 12

__________ is the step in data mining that includes addressing missing and erroneous data, reducing the number of variables, defining new variables, and data exploration.

Accepted Answer

A) Data sampling 
B) Data partitioning 
C) Data preparation 
D) Model assessment 
A) Data sampling 
B) Data partitioning 
C) Data preparation 
D) Model assessment

Question 13

Data used to build a data mining model is called

Accepted Answer

A) validation data. 
B) training data. 
C) test data. 
D) exploration data. 
A) validation data. 
B) training data. 
C) test data. 
D) exploration data.

Question 14

_________ attempts to classify a categorical outcome as a linear function of explanatory variables.

Accepted Answer

A) Linear regression 
B) Logistic regression 
C) Classification model 
D) Supervised learning 
A) Linear regression 
B) Logistic regression 
C) Classification model 
D) Supervised learning

Question 15

In the k-nearest neighbors method, when the value of k is set to 1

Accepted Answer

A) the classification or prediction of a new observation is based solely on the single most similar observation from the training set. 
B) the new observation's class is naïvely assigned to the most common class in the training set. 
C) the new observation's prediction is used to estimate the anticipated error rate on future data over the entire training set. 
D) the classification or prediction of a new observation is subject to the smallest possible classification error. 
A) the classification or prediction of a new observation is based solely on the single most similar observation from the training set. 
B) the new observation's class is naïvely assigned to the most common class in the training set. 
C) the new observation's prediction is used to estimate the anticipated error rate on future data over the entire training set. 
D) the classification or prediction of a new observation is subject to the smallest possible classification error.

Question 16

Misclassifying an actual __________ observation as a(n) __________ observation is known as a false positive.

Accepted Answer

A) Class 0, Class 1 
B) Class 1, Class 0 
C) error, accuracy 
D) false, true 
A) Class 0, Class 1 
B) Class 1, Class 0 
C) error, accuracy 
D) false, true

Question 17

Which of the following is a commonly used supervised learning method?

Accepted Answer

A) k-means clustering 
B) k-nearest neighbors 
C) hierarchical clustering 
D) association rule development 
A) k-means clustering 
B) k-nearest neighbors 
C) hierarchical clustering 
D) association rule development

Question 18

__________ refers to the scenario in which the analyst builds a model that does a great job of explaining the sample of data on which it is based but fails to accurately predict outside the sample data.

Accepted Answer

A) Underfitting 
B) Overfitting 
C) Oversampling 
D) Undersampling 
A) Underfitting 
B) Overfitting 
C) Oversampling 
D) Undersampling

A test set is the data set used to

A(n) __________ is often displayed as a row of values in a spreadsheet or database in which the columns correspond to the variables.

__________ involves descriptive statistics, data visualization, and clustering.

The y-axis of a decile chart shows

__________ is a generalization of linear regression for predicting a categorical outcome variable.

A __________ classifies a categorical outcome variable by splitting observations into groups via a sequence of hierarchical rules.

__________ is the manipulation of the data with the goal of putting it in a form suitable for formal modeling.

A characteristic or quantity of interest that can take on different values is a(n)

Estimation methods are also referred to as

__________ is a measure of the heterogeneity of observations in a classification tree.

The x-axis of a lift chart shows

__________ is the step in data mining that includes addressing missing and erroneous data, reducing the number of variables, defining new variables, and data exploration.

Data used to build a data mining model is called

_________ attempts to classify a categorical outcome as a linear function of explanatory variables.

In the k-nearest neighbors method, when the value of k is set to 1

Misclassifying an actual observation as a(n) observation is known as a false positive.

Which of the following is a commonly used supervised learning method?

__________ refers to the scenario in which the analyst builds a model that does a great job of explaining the sample of data on which it is based but fails to accurately predict outside the sample data.

Introduction

Descriptive Statistics

Data Visualization

Descriptive Data Mining

Probability: an Introduction to Modeling Uncertainty

Statistical Inference

Linear Regression

Time Series Analysis and Forecasting

Spreadsheet Models

Monte Carlo Simulation

Linear Optimization Models

Integer Linear Optimization Models

Nonlinear Optimization Models

Decision Analysis

Filters

Exam 9: Predictive Data Mining

A test set is the data set used to

A(n) __________ is often displayed as a row of values in a spreadsheet or database in which the columns correspond to the variables.

__________ involves descriptive statistics, data visualization, and clustering.

The y-axis of a decile chart shows

__________ is a generalization of linear regression for predicting a categorical outcome variable.

A __________ classifies a categorical outcome variable by splitting observations into groups via a sequence of hierarchical rules.

__________ is the manipulation of the data with the goal of putting it in a form suitable for formal modeling.

A characteristic or quantity of interest that can take on different values is a(n)

Estimation methods are also referred to as

__________ is a measure of the heterogeneity of observations in a classification tree.

The x-axis of a lift chart shows

__________ is the step in data mining that includes addressing missing and erroneous data, reducing the number of variables, defining new variables, and data exploration.

Data used to build a data mining model is called

_________ attempts to classify a categorical outcome as a linear function of explanatory variables.

In the k-nearest neighbors method, when the value of k is set to 1

Misclassifying an actual __________ observation as a(n) __________ observation is known as a false positive.

Which of the following is a commonly used supervised learning method?

__________ refers to the scenario in which the analyst builds a model that does a great job of explaining the sample of data on which it is based but fails to accurately predict outside the sample data.

Introduction

Descriptive Statistics

Data Visualization

Descriptive Data Mining

Probability: an Introduction to Modeling Uncertainty

Statistical Inference

Linear Regression

Time Series Analysis and Forecasting

Spreadsheet Models

Monte Carlo Simulation

Linear Optimization Models

Integer Linear Optimization Models

Nonlinear Optimization Models

Decision Analysis

Filters

Misclassifying an actual observation as a(n) observation is known as a false positive.