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ISYE 6501 Spring Exam 1 Questions And Answers( Complete Solution)
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ISYE 6501 Spring Exam 1 Questions And Answers( Complete Solution)
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ISYE 6501 Spring Exam 1What is modeling? –
Describing a real-life situation mathematically, analyzing the math and then
turning the math back into a real-life situation.
What is a data point? –
A row of data. All of the information about one observation.
What are some (4) names for columns of data? –
Attributes, features, covariates, predictors
Name some common types of structured data (5) –
Quantitative, categorical, binary, unrelated, time series
What is binary data? –
A subset of categorical data (although can be treated as numerical) that can only
take on one value (Y/N, M/F, etc.)
What is unstructured data? Example? –
Data that is not easily described or stored. Ex: Text.
When do you use a soft classifier? –
When you can't draw a line to divide all data points.
What are support vectors? –
Points supporting a shape on parallel lines.
SVM - what does it mean if the coefficients are near zero? –
Those coefficients are probably not relevant for classification.
SVM - Does a classifier need to be a straight line? –
No. Kernel methods allow for nonlinear classifiers.
What is the most common scaling used? –
Scaling data between 0 and 1.
What is standardization? –
Scaling data to a normal distribution (typically mean = 0, sd = 1)
When might you use scaling over standardization? –
When your data needs to be in a bounded range. Ex: neural networks,
optimization models, etc.
, What are two models that work better with standardization over scaling? –
Principle component analysis and clustering.
How does KNN determine what a new point's class will be? –
The new data point's class is the most common class among the k neighbors
What is the most common method to measure distance between k nearest neighbors? –
A straight line, although other methods can be used
How can you adjust KNN for attributes that are more important for the
classification? - Weight the distances and give those attributes more weight
What type of effects explain why we can't measure the model's effectiveness on training
data? –
Random effects
Why is the observed performance on high performing models probably too optimistic? –
High performing models are most likely to have benefited from random effects.
Which set of data is used to choose the best model –
Validation set
Which set of data is used for building models? –
Training set
Which set of data is used for measuring a model's performance? –
Test set
Name two ways to split the data into training and test sets –
Random and rotation
Given a set of time series data, which might be a better way to split the data into training
and test sets and why? Random or rotation? –
Rotation because rotation will spread the data equally and random could select
more values from one year
Which should we use the majority of our data for: training, validation or test? –
Training - most experts recommend using 50-70% for training and splitting the
rest equally between validation and test.
What is a solution to the problem of more important data points appearing in only
validation or test sets? –
Cross validation
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