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ISYE 6501 Mid Term 1.
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ISYE 6501 Mid Term 1.
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ISYE 6501 Mid Term 1What is modeling? - ANS-Describing a real life situation mathematically
Model - ANS-Mathematical approaches to solving analytics problems
1. Regression for ex. can be called the model of choice.
2. If more detail (descriptive and numerical), the use of, for ex. Regression, can be
called the model.
3. The actual values and output along with 1 and 2 can be referred to as the model.
Cross Cutting Concepts - ANS-Preparing data, measuring the quality of a model's
output, missing data, etc..
Classification - ANS-The process of grouping things based on their similarities
Ex. Classifying consumers by who is likely to pay back a loan and who is not
Soft Classifier - ANS-A soft classifier is used when it is not possible to completely
separate two categories of data points via the classifying line.
the soft classifier gives as good of a separation as possible rather than a hard classifier
that separates perfectly.
Classifier Rules - ANS-Must define the best separator. Misclassifying a data point may
be more detrimental than misclassifying another. the more costly one bad decision is,
the more we want to move away from it.
Its okay to push the classifier in one direction so that it is conservative if stakes are
higher.
Data Point - ANS-Every Row, a single observation of information
Attribute/Feature/Covariate/Predictor - ANS-Piece of information, column of data
Responses/Outcomes - ANS-Special type of column known as being the answer for
each data point
Structured Data - ANS-Can be easily described and stored
Unstructured Data - ANS-Usually in form of text, cannot be easily described or stored
, Quantitative Data - ANS-Most numerical data, numbers have a meaning
Non Quantitative Data - ANS-Descriptive data, Categorical data, any data without
numerical meaning ex. zip codes
Categorical Data - ANS-numerical data that does not have quantitative meaning
Binary Data - ANS-Subset of categorical data, only 2 values: M/F, On/Off
Unrelated data - ANS-No relationships such as applicants or customer
Time series data - ANS-same data recorded over time, often at different intervals
Coefficient - ANS-a numerical or constant quantity placed before and multiplying the
variable in an algebraic expression
Support Vector Machine - ANS-A discriminative classifier formally defined by a
separating hyperplane. In other words, given labeled training data (supervised learning),
the algorithm outputs an optimal hyperplane which categorizes new data points.
SVM margin - ANS-1. the line is defined by a set of coefficients a1 through am for each
attribute, and an intercept a0.
2. We want to find values of a0 and a1, up to am that classify the points correctly and
have the maximum gap or margin between the parallel lines.
3. For each point j we can calculate the error in its calculation. Maximizing the margin
involves minimizing the sum over all factors of aj squared.
What does the name Support Vector machine mean? - ANS-Support Vector: Convex
Hull-if we connect the outermost points of data, a line crossing through one of those
points is a support, or support vector. A support vector can be anywhere around the
shape and more than one support vector can exist.
Machine: Comes from the modeling portion in computer programing.
Classifiers in Advanced SVM - ANS-1. We can shift classifiers based on the stakes
involved in the given situation.
2. A classifier does not have to be a straight line.
Kernal Methods - ANS-In machine learning, kernel methods are a class of algorithms for
pattern analysis, whose best known member is the support vector machine (SVM). The
general task of pattern analysis is to find and study general types of relations (for
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