Question 1

The probability of a hypothesis before the presentation of evidence.

Accepted Answer

A)  a priori 
B)  subjective 
C)  posterior 
D)  conditional 
A)  a priori 
B)  subjective 
C)  posterior 
D)  conditional A

Question 2

The table below contains counts and ratios for a set of data instances to be used for supervised Bayesian learning. The output attribute is sex with possible values male and female. Consider an individual who has said no to the life insurance promotion, yes to the magazine promotion, yes to the watch promotion and has credit card insurance. Use the values in the table together with Bayes classifier to determine which of a,b,c or d represents the probability that this individual is male.

Accepted Answer

A)  4/6) 2/6) 2/6) 2/6) 6/10) / PE 
B)  4/6) 2/6) 3/4) 2/6) 3/4) / PE 
C)  4/6) 2/6) 4/6) 2/6) 6/10) / PE 
D)  2/6) 4/6) 4/6) 2/6) 4/10) / PE 
A)  4/6) 2/6) 2/6) 2/6) 6/10) / PE 
B)  4/6) 2/6) 3/4) 2/6) 3/4) / PE 
C)  4/6) 2/6) 4/6) 2/6) 6/10) / PE 
D)  2/6) 4/6) 4/6) 2/6) 4/10) / PE C

Question 3

Logistic regression is a ________ regression technique that is used to model data having a _____outcome.

Accepted Answer

A)  linear, numeric 
B)  linear, binary 
C)  nonlinear, numeric 
D)  nonlinear, binary 
A)  linear, numeric 
B)  linear, binary 
C)  nonlinear, numeric 
D)  nonlinear, binary D

Question 4

Machine learning techniques differ from statistical techniques in that machine learning methods

Accepted Answer

A)  typically assume an underlying distribution for the data. 
B)  are better able to deal with missing and noisy data. 
C)  are not able to explain their behavior. 
D)  have trouble with large-sized datasets. 
A)  typically assume an underlying distribution for the data. 
B)  are better able to deal with missing and noisy data. 
C)  are not able to explain their behavior. 
D)  have trouble with large-sized datasets.

Question 5

This unsupervised clustering algorithm terminates when mean values computed for the current iteration of the algorithm are identical to the computed mean values for the previous iteration.

Accepted Answer

A)  agglomerative clustering 
B)  conceptual clustering 
C)  K-Means clustering 
D)  expectation maximization 
A)  agglomerative clustering 
B)  conceptual clustering 
C)  K-Means clustering 
D)  expectation maximization

Question 6

This technique associates a conditional probability value with each data instance.

Accepted Answer

A)  linear regression 
B)  logistic regression 
C)  simple regression 
D)  multiple linear regression 
A)  linear regression 
B)  logistic regression 
C)  simple regression 
D)  multiple linear regression

Question 7

The leaf nodes of a model tree are

Accepted Answer

A)  averages of numeric output attribute values. 
B)  nonlinear regression equations. 
C)  linear regression equations. 
D)  sums of numeric output attribute values. 
A)  averages of numeric output attribute values. 
B)  nonlinear regression equations. 
C)  linear regression equations. 
D)  sums of numeric output attribute values.

Question 8

This clustering algorithm initially assumes that each data instance represents a single cluster.

Accepted Answer

A)  agglomerative clustering 
B)  conceptual clustering 
C)  K-Means clustering 
D)  expectation maximization 
A)  agglomerative clustering 
B)  conceptual clustering 
C)  K-Means clustering 
D)  expectation maximization

Question 9

Simple regression assumes a __________ relationship between the input attribute and output attribute.

Accepted Answer

A)  linear 
B)  quadratic 
C)  reciprocal 
D)  inverse 
A)  linear 
B)  quadratic 
C)  reciprocal 
D)  inverse

Question 10

This supervised learning technique can process both numeric and categorical input attributes.

Accepted Answer

A)  linear regression 
B)  Bayes classifier 
C)  logistic regression 
D)  backpropagation learning 
A)  linear regression 
B)  Bayes classifier 
C)  logistic regression 
D)  backpropagation learning

Question 11

Regression trees are often used to model _______ data.

Accepted Answer

A)  linear 
B)  nonlinear 
C)  categorical 
D)  symmetrical 
A)  linear 
B)  nonlinear 
C)  categorical 
D)  symmetrical

Question 12

This clustering algorithm merges and splits nodes to help modify nonoptimal partitions.

Accepted Answer

A)  agglomerative clustering 
B)  expectation maximization 
C)  conceptual clustering 
D)  K-Means clustering 
A)  agglomerative clustering 
B)  expectation maximization 
C)  conceptual clustering 
D)  K-Means clustering

Question 13

With Bayes classifier, missing data items are

Accepted Answer

A)  treated as equal compares. 
B)  treated as unequal compares. 
C)  replaced with a default value. 
D)  ignored. 
A)  treated as equal compares. 
B)  treated as unequal compares. 
C)  replaced with a default value. 
D)  ignored.

The probability of a hypothesis before the presentation of evidence.

Logistic regression is a ____ regression technique that is used to model data having a _outcome.

Machine learning techniques differ from statistical techniques in that machine learning methods

This unsupervised clustering algorithm terminates when mean values computed for the current iteration of the algorithm are identical to the computed mean values for the previous iteration.

This technique associates a conditional probability value with each data instance.

The leaf nodes of a model tree are

This clustering algorithm initially assumes that each data instance represents a single cluster.

Simple regression assumes a __________ relationship between the input attribute and output attribute.

This supervised learning technique can process both numeric and categorical input attributes.

Regression trees are often used to model _______ data.

This clustering algorithm merges and splits nodes to help modify nonoptimal partitions.

With Bayes classifier, missing data items are

Data Mining: a First View

Data Mining: a Closer Look

Basic Data Mining Techniques

An Excel-Based Data Mining Tool

Knowledge Discovery in Databases

The Data Warehouse

Formal Evaluation Techniques

Neural Networks

Building Neural Networks With Ida

Specialized Techniques

Rule-Based Systems

Managing Uncertainty in Rule-Based Systems

Intelligent Agents

Filters

Exam 10: Statistical Techniques

The probability of a hypothesis before the presentation of evidence.

Logistic regression is a ________ regression technique that is used to model data having a _____outcome.

Machine learning techniques differ from statistical techniques in that machine learning methods

This unsupervised clustering algorithm terminates when mean values computed for the current iteration of the algorithm are identical to the computed mean values for the previous iteration.

This technique associates a conditional probability value with each data instance.

The leaf nodes of a model tree are

This clustering algorithm initially assumes that each data instance represents a single cluster.

Simple regression assumes a __________ relationship between the input attribute and output attribute.

This supervised learning technique can process both numeric and categorical input attributes.

Regression trees are often used to model _______ data.

This clustering algorithm merges and splits nodes to help modify nonoptimal partitions.

With Bayes classifier, missing data items are

Data Mining: a First View

Data Mining: a Closer Look

Basic Data Mining Techniques

An Excel-Based Data Mining Tool

Knowledge Discovery in Databases

The Data Warehouse

Formal Evaluation Techniques

Neural Networks

Building Neural Networks With Ida

Specialized Techniques

Rule-Based Systems

Managing Uncertainty in Rule-Based Systems

Intelligent Agents

Filters

Logistic regression is a ____ regression technique that is used to model data having a _outcome.