CIS QUIZ 4
What is a major drawback to the basic majority voting classification in kNN
Classes with more frequent examples tend to dominate prediction.
What is a major drawback to the basic majority voting classification in kNN?
Classes with more frequent examples tend to dominate prediction.
A disadvantages of Hopfield neural networks is that their structure cannot be replicated on an electronic circuit board.
False
Generally speaking, support vector machines are less accurate a prediction method than other approaches such as decision trees and neural networks.
False
In the mining industry case study, the input to the neural network is a verbal description of a hanging rock on the mine wall.
False
In the opening vignette, the high accuracy of the models in predicting the outcomes of complex medical procedures showed that data mining tools are ready to replace experts in the medical field.
False
The Naïve Bayes method is a powerful tool for representing dependency structure in a graphical, explicit, and intuitive way.
False
The k-nearest neighbor algorithm is overly complex when compared to artificial neural networks and support vector machines.
False
The strong assumption of independence among the input variables in the Naïve Bayes method is realistic.
False
UnansweredQuestion 40 / 1 pts Compared to the human brain, artificial neural networks have many more neurons.
False
Using support vector machines, you must normalize the data before you numericize it.
False
________ has proved the most popular of the techniques proposed for shedding light into the "black-box" characterization of trained neural networks.
Sensitivity analysis
Naïve Bayes is a simple probability-based classification method derived from the Bayes theorem.
True
Neural networks represent a brain metaphor for information processing. These models are biologically inspired rather than an exact replica of how the brain actually functions.
True
Pearl won the prestigious ACM's A.M. Turing Award for his contributions to the field of artificial intelligence and the development of BN.
True
The k-nearest neighbor algorithm appears well-suited to solving image recognition and categorization problems.
True
The most complex problems solved by neural networks require one or more hidden layers for increased accuracy.
True
The use of hidden layers and new topologies and algorithms renewed waning interest in neural networks.
True
Unlike other "black box" predictive models, support vector machines have a solid mathematical foundation in statistical learning theory.
True
Which of the following are advantages of the Naïve Bayes method or classification?
absent any underlying assumptions that may affect output
Why is sensitivity analysis frequently used for artificial neural networks?
because it is generally informative, and can be used to identify a preferred model if multiple models exist
Some of the benefits of the BN model include:
both ease of adaptability and extent of applicability.
In an ANN, ________ express the relative strength (or mathematical value) of the input data or the many connections that transfer data from layer to layer
connection weights
In an ANN, ________ express the relative strength (or mathematical value) of the input data or the many connections that transfer data from layer to layer.
connection weights
When using support vector machines, in which stage do you select the kernel type (e.g., RBF, Sigmoid)?
developing the model
The opening vignette teaches us that ________ medicine is a relatively new term coined in the healthcare arena, where the main idea is to dig deep into past experiences to discover new and useful knowledge to improve medical and managerial procedures in healthcare.
evidence-based
For how long do SVM models continue to be accurate and actionable?
for as long as the behavior of the domain stays the same
BN is a powerful tool for representing dependency structure in a ________, explicit, and intuitive way.
graphical
In a typical network structure of an ANN consisting of three layers—input, intermediate, and output—the intermediate layer is called the ________ layer.
hidden
Which element in an artificial neural network roughly corresponds to a dendrite in a human brain?
input
In the mathematical formulation of SVM's, the normalization and/or scaling are important steps to guard against variables/attributes with ________ that might otherwise dominate the classification formulae.
larger variance
Neural computing refers to a ________ methodology for machine learning.
pattern-recognition
A thorough analysis of an early neural network model called the ________, which used no hidden layer, in addition to a negative evaluation of the research potential by Minsky and Papert in 1969, led to a diminished interest in neural networks.
perceptron
In the power generators case study, data mining-driven software tools, including data-driven ________ technologies with historical data, helped an energy company reduce emissions of NOx and CO.
predictive modeling
When using support vector machines, in which stage do you transform the data?
preprocessing the data
Using the k-nearest neighbor machine learning algorithm for classification, larger values of k:
reduce the effect of noise on the classification.
The k-nearest neighbor machine learning algorithm (kNN) is:
regarded as a "lazy" learning method.
The random forest (RF) model is a modification to what algorithm?
simple bagging
Writing the SVM classification rule in its dual form reveals that classification is only a function of the ________, i.e., the training data that lie on the margin.
support vectors
All of the following are disadvantages/limitations of the SVM technique EXCEPT
their accuracy is poor in many domains compared to neural networks.
All of the following are disadvantages/limitations of the SVM technique EXCEPT:
their accuracy is poor in many domains compared to neural networks.
Support vector machines are a popular machine learning technique primarily because of:
their superior predictive power and their theoretical foundation.
Which element in an artificial neural network roughly corresponds to a synapse in a human brain?
weight
