Exam 5: Machine-Learning Techniques for Predictive Analytics

The methodology employed in the traffic case follows a very well-known standardized analytics process know by its acronym _________.

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.

A disadvantages of Hopfield neural networks is that their structure cannot be replicated on an electronic circuit board.

The k-nearest neighbor algorithm appears well-suited to solving image recognition and categorization problems.

Bagging can be used for:

The Naïve Bayes method is a powerful tool for representing dependency structure in a graphical, explicit, and intuitive way.

The most complex problems solved by neural networks require one or more hidden layers for increased accuracy.

Model ensembles tend to be more ________ against outliers and noise in the data set than individual models.

The strong assumption of independence among the input variables in the Naïve Bayes method is realistic.

Naïve Bayes is a simple probability-based classification method derived from the Bayes theorem.

Some of the benefits of the BN model include:

Ensemble models can be quickly characterized based on their use of a bagging or boosting method type.

Pearl won the prestigious ACM's A.M. Turing Award for his contributions to the field of artificial intelligence and the development of BN.

BN is a powerful tool for representing dependency structure in a ________, explicit, and intuitive way.

In the mining industry case study, the input to the neural network is a verbal description of a hanging rock on the mine wall.

The k-nearest neighbor algorithm is overly complex when compared to artificial neural networks and support vector machines.

The random forest (RF) model is a modification to what algorithm?

Backpropagation requires the of vector pairs, with the pairs consisting of:

In 1992, Boser, Guyon, and Vapnik suggested a way to create nonlinear classifiers by applying the kernel trick to maximum-margin hyperplanes. How does the resulting algorithm differ from the original optimal hyperplane algorithm proposed by Vladimir Vapnik in 1963?

Homogeneous-type ensembles combine the outcomes of: