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ISYE 6501 Midterm 1 Exam With Complete Solutions 2024
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ISYE 6501 Midterm 1 Exam With Complete Solutions 2024
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ISYE 6501 Midterm 1 Exam With CompleteSolutions 2024
Rows .- .correct .answer.Data .points .are .values .in .data .tables
Columns .- .correct .answer.The .'answer' .for .each .data .point .(response/outcome)
Structured .Data .- .correct .answer.Quantitative, .Categorical, .Binary, .Unrelated, .Time
.Series
Unstructured .Data .- .correct .answer.Text
Support .Vector .Model .- .correct .answer.Supervised .machine .learning .algorithm .used .for
.both .classification .and .regression .challenges. .
Mostly .used .in .classification .problems .by .plotting .each .data .item .as .a .point .in .n-
dimensional .space .(n .is .the .number .of .features .you .have) .with .the .value .of .each .feature
.being .the .value .of .a .particular .coordinate. .
Then .you .classify .by .finding .a .hyperplane .that .differentiates .the .2 .classes .very .well.
.Support .vectors .are .simply .the .coordinates .of .individual .observation .-- .it .best .segregates
.the .two .classes .(hyperplane ./ .line).
What .do .you .want .to .find .with .a .SVM .model? .- .correct .answer.Find .values .of .a0, .a1,...,up
.to .am .that .classifies .the .points .correctly .and .has .the .maximum .gap .or .margin .between
.the .parallel .lines.
What .should .the .sum .of .the .green .points .in .a .SVM .model .be? .- .correct .answer.The .sum
.of .green .points .should .be .greater .than .or .equal .to .1
What .should .the .sum .of .the .red .points .in .a .SVM .model .be? .- .correct .answer.The .sum .of
.red .points .should .be .less .than .or .equal .to .-1
What .should .the .total .sum .of .green .and .red .points .be? .- .correct .answer.The .total .sum .of
.all .green .and .red .points .should .be .equal .to .or .greater .than .1 .because .yj .is .1 .for .green .and
.-1 .for .red.
First .principal .component .- .correct .answer.PCA .-- .a .linear .combination .of .original
.predictor .variables .which .captures .the .maximum .variance .in .the .data .set. .It .determines
, .the .direction .of .highest .variability .in .the .data. .Larger .the .variability .captured .in .first
.component, .larger .the .information .captured .by .component. .No .other .component .can
.have .variability .higher .than .first .principal .component.
it .minimizes .the .sum .of .squared .distance .between .a .data .point .and .the .line.
Second .principal .component .- .correct .answer.PCA .-- .also .a .linear .combination .of .original
.predictors .which .captures .the .remaining .variance .in .the .data .set .and .is .uncorrelated .with
.Z¹. .In .other .words, .the .correlation .between .first .and .second .component .should .is .zero.
What .if .it's .not .possible .to .separate .green .and .red .points .in .a .SVM .model? .- .correct
.answer.Utilize .a .soft .classifier .-- .In .a .soft .classification .context, .we .might .add .an .extra
.multiplier .for .each .type .of .error .with .a .larger .penalty, .the .less .we .want .to .accept .mis-
classifying .that .type .of .point.
Soft .Classifier .- .correct .answer.Account .for .errors .in .SVM .classification. .Trading .off
.minimizing .errors .we .make .and .maximizing .the .margin.
To .trade .off .between .them, .we .pick .a .lambda .value .and .minimize .a .combination .of .error
.and .margin. .As .lambda .gets .large, .this .term .gets .large.
The .importance .of .a .large .margin .outweighs .avoiding .mistakes .and .classifying .known
.data .points.
Should .you .scale .your .data .in .a .SVM .model? .- .correct .answer.Yes, .so .the .orders .of
.magnitude .are .approximately .the .same.
Data .must .be .in .bounded .range.
Common .scaling: .data .between .0 .and .1
a. .Scale .factor .by .factor
b. .Linearly
How .should .you .find .which .coefficients .to .hold .value .in .a .SVM .model? .- .correct .answer.If
.there .is .a .coefficient .who's .value .is .very .close .to .0, .means .the .corresponding .attribute .is
.probably .not .relevant .for .classification.
Does .SVM .work .the .same .for .multiple .dimensions? .- .correct .answer.Yes
Does .a .SVM .classifier .need .to .be .a .straight .line? .- .correct .answer.No, .SVM .can .be
.generalized .using .kernel .methods .that .allow .for .nonlinear .classifiers. .Software .has .a
.kernel .SVM .function .that .you .can .use .to .solve .for .both .linear .and .nonlinear .classifiers.
Can .classification .questions .be .answered .as .probabilities .in .SVM? .- .correct .answer.Yes.
K .Nearest .Neighbor .Algorithm .- .correct .answer.Find .the .class .of .the .new .point, .Pick .the .k
.closest .points .to .the .new .one, .the .new .points .class .is .the .most .common .amongst .the .k
.neighbors.
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