ISYE 6501 final practise with correct
solutions.
, Support Vector Machine - correct ans:A supervised learning, classification
model.
Uses extremes, or identified points in the data from which margin vectors are
placed against. The hyperplane between these vectors is the classifier
SVM Pros/Cons - correct ans:Pros: It works really well with a clear margin of
separation
It is effective in high dimensional spaces.
It is effective in cases where the number of dimensions is greater than the
number of samples.
It uses a subset of training points in the decision function (called support
vectors), so it is also memory efficient.
Cons: Not good for very large data sets
Not good for when the data set has more noise i.e. target classes are
overlapping
Doesn't directly provide probability estimates.
K-nearest neighbor (K-NN) - correct ans:An unsupervised classification
algorithm. Looks at the X number of closest points to the new one and
classifies as whichever is most common.
K-nearest neighbor (K-NN) Pros/Cons - correct ans:Pros: No assumptions
about data
Easy to understand/Interpret
Varsatile
Cons: Computationally expensive because algorithm stores all training data
Sensitive to irrelevant features and scale of data
k-fold cross validation - correct ans:Validation Technique where data is
divided into X number of data subsets. Each subset is then used as a for
solutions.
, Support Vector Machine - correct ans:A supervised learning, classification
model.
Uses extremes, or identified points in the data from which margin vectors are
placed against. The hyperplane between these vectors is the classifier
SVM Pros/Cons - correct ans:Pros: It works really well with a clear margin of
separation
It is effective in high dimensional spaces.
It is effective in cases where the number of dimensions is greater than the
number of samples.
It uses a subset of training points in the decision function (called support
vectors), so it is also memory efficient.
Cons: Not good for very large data sets
Not good for when the data set has more noise i.e. target classes are
overlapping
Doesn't directly provide probability estimates.
K-nearest neighbor (K-NN) - correct ans:An unsupervised classification
algorithm. Looks at the X number of closest points to the new one and
classifies as whichever is most common.
K-nearest neighbor (K-NN) Pros/Cons - correct ans:Pros: No assumptions
about data
Easy to understand/Interpret
Varsatile
Cons: Computationally expensive because algorithm stores all training data
Sensitive to irrelevant features and scale of data
k-fold cross validation - correct ans:Validation Technique where data is
divided into X number of data subsets. Each subset is then used as a for
