Upload
wake-tech-bas
View
27
Download
1
Embed Size (px)
Citation preview
BAS 250Lesson 6: Neural Networks
This Week’s Learning Objectives
• Explain what a neural network is, how it is used, and
the benefits of using it
• Recognize the necessary format for data in neural
network data mining
• Interpret a neural network’s outputs and understand
how to apply them to a scoring data set for deployment
Neural networks, as the name implies, try to
mimic interconnected neurons in the brain in
order to make the algorithm capable of
complex learning for extracting patterns and
detecting trends.
Introduction to Neural Networks
• Neural networks can predict categories or classifications similarly to decision trees; however, they are better at finding the strength of connections between attributes
• Neural networks are not as limited regarding value ranges as some other methodologies
Introduction to Neural Networks
It is built upon the premise that real world data structures are complex, and thus it necessitates complex learning systems.
Usually regression is “one-shot”; you cannot “train” a regression model. In other words, regression cannot “learn”.
Neural Networks
A trained neural network can be viewed as an “expert” in
the category of information it has been given to analyze.
This expert system can provide projections given new
solutions to a problem and answer "what if" questions.
Flexible models for regression and classification
Higher predictive power than regression and classification
trees
Benefits of Neural Networks
• Neural network algorithms often employ a concept
called fuzzy logic • Fuzzy logic is an inferential, probability-based approach to
data comparisons
• Using fuzzy logic, one can infer, based on probabilities, the
strength of the relationship between attributes in data sets
Fuzzy Logic of Neural Networks
• Neural networks use what is called a ‘hidden layer’ to
compare all attributes in a data set to all other attributes
• Circles in the neural network graph are nodes
• Lines between nodes are called neurons• The thicker and darker the neuron is between nodes, the
stronger the affinity between those nodes.
Structure of Neural Networks
A typical neural network is composed of three
types of layers
o input layer: data
o hidden layer: data transformation and manipulation
o output layer
Structure of Neural Networks
A graphical view
of a neural
network, in
RapidMiner,
showing different
strength neurons
Structure of Neural Networks
• The number of hidden layers can be adjusted in a neural network using
the size parameter
• There is no hard-and-fast rule about the size one should use, but a
generally accepted guideline is that it should be a number between
the total number of variables and the number of values in the
dependent variable
Neural Networks Rules of Thumb
• The number of maximum iterations can also be adjusted during model
development to identify the maximum number of times a neural network
will run through comparisons of attribute values against the target
attribute’s values
• If the independent variables are good predictors of the dependent
variable, most neural network models will achieve convergence
before reaching the maximum number of iterations
Neural Networks Rules of Thumb
Convergence is when the model has
successfully developed profiles based on the
training observations that can be used to make
predictions for scoring data
Convergence
There are three types of layers, not three layers, in the
network. There may be more than one hidden layer and it
depends on how complex the researcher wants the model to
be.
Because the input and the output are mediated by the hidden
layer, neural networks are commonly seen as a “black box.”
Harder to interpret and understand
Drawbacks
Use it when predictive accuracy is the most important
objective
When you need a non-linear fit but do not want over-fitting
and want to avoid the tedious work
When you have mixed data type, such as nominal, ordinal,
and continuous, but want to avoid the laborious data
transformation
Recommendations
Summary - Learning Objectives• Explain what a neural network is, how it is used, and
the benefits of using it
• Recognize the necessary format for data in neural
network data mining
• Interpret a neural network’s outputs and understand
how to apply them to a scoring data set for deployment
“This workforce solution was funded by a grant awarded by the U.S. Department of Labor’s
Employment and Training Administration. The solution was created by the grantee and does not
necessarily reflect the official position of the U.S. Department of Labor. The Department of Labor
makes no guarantees, warranties, or assurances of any kind, express or implied, with respect to such
information, including any information on linked sites and including, but not limited to, accuracy of the
information or its completeness, timeliness, usefulness, adequacy, continued availability, or
ownership.”
Except where otherwise stated, this work by Wake Technical Community College Building Capacity in
Business Analytics, a Department of Labor, TAACCCT funded project, is licensed under the Creative
Commons Attribution 4.0 International License. To view a copy of this license, visit
http://creativecommons.org/licenses/by/4.0/
Copyright Information