Athabasca University (AU ) • Data Mining
Las últimas cargas en Data Mining @ Athabasca University (AU ). ¿Buscas apuntes de Data Mining en Athabasca University (AU )? Tenemos un montón de apuntes, guías de estudio y notas de estudio disponibles para Data Mining en Athabasca University (AU ).
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Cursos Data Mining @ Athabasca University (AU )
Notas disponibles para los cursos siguiente de Data Mining en Athabasca University (AU )
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COMP 682 COMP 682 2
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COMP 682 Data Mining COMP 682 2
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COMP682 1
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COMP682 Data Mining 1
Último contenido Athabasca University (AU ) • Data Mining
1 
What types of inputs do each of the 3 types of decision trees take? 
ID3: categorical only 
C4.5: numeric and categorical 
CART: numeric and categorical 
 
2 
How do each of the 3 types of decision trees split? 
ID3: max information gain and minimum entropy 
C4.5: information gain and gain ratio 
CART: Gini index and twoing creation 
 
3 
How do each of the 3 types of decision trees handle missing data? 
ID3: does not 
C4.5: omits from calculations 
CART: surrogate splits 
 
4 
What is CBA? 
...
- Examen
- • 6 páginas's •
-
Athabasca University•COMP682 Data Mining
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1 
What types of inputs do each of the 3 types of decision trees take? 
ID3: categorical only 
C4.5: numeric and categorical 
CART: numeric and categorical 
 
2 
How do each of the 3 types of decision trees split? 
ID3: max information gain and minimum entropy 
C4.5: information gain and gain ratio 
CART: Gini index and twoing creation 
 
3 
How do each of the 3 types of decision trees handle missing data? 
ID3: does not 
C4.5: omits from calculations 
CART: surrogate splits 
 
4 
What is CBA? 
...
1 
Factors that influence the organization of requirements 
-nature and scope of the system under development; 
-Techniques and strategies used to elicit and capture reqts; 
-Tools and environments employed 
 
2 
Organizing by Subsystem 
Complex systems can only be visualized and built as systems of subsystems; 
1st build system-level reqts wo/ref to subsystems; 
Refine into subsystems/define interfaces; 
Hierarchy of requirements 
 
3 
Organizing by Product Families 
Common functionality across...
- Examen
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Athabasca University•Comp 682
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1 
Factors that influence the organization of requirements 
-nature and scope of the system under development; 
-Techniques and strategies used to elicit and capture reqts; 
-Tools and environments employed 
 
2 
Organizing by Subsystem 
Complex systems can only be visualized and built as systems of subsystems; 
1st build system-level reqts wo/ref to subsystems; 
Refine into subsystems/define interfaces; 
Hierarchy of requirements 
 
3 
Organizing by Product Families 
Common functionality across...
1 
What is the goal of software development? 
"-Develop quality software, -Meet schedule, -Meet budget, -Satisfy customer needs (but customers are varied)" 
 
2 
What is the relationship between requirements engineering and project success? 
"The Standish Group 1994: 1/3 of all projects cancelled and 1/2 overbudget, Reqts errors most common class of error, Reqts errors most expensive errors to fix (multiplicitive effect/rework), Better reqts engr => better project success" 
 
3 
S...
- Examen
- • 10 páginas's •
-
Athabasca University•COMP 682
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1 
What is the goal of software development? 
"-Develop quality software, -Meet schedule, -Meet budget, -Satisfy customer needs (but customers are varied)" 
 
2 
What is the relationship between requirements engineering and project success? 
"The Standish Group 1994: 1/3 of all projects cancelled and 1/2 overbudget, Reqts errors most common class of error, Reqts errors most expensive errors to fix (multiplicitive effect/rework), Better reqts engr => better project success" 
 
3 
S...
P(E) is assumed to be the same for all ___ ______ (Naive Bayes) 
Naive Bayes Pros 
- Despite strict independence assumptions, performs surprisingly well for classification on real-world tasks 
- Natural and incremental learner. Needs not reprocess all past training examples when new data arrive 
- Fast, efficient, and effective 
quantile(titanic$Age, seq(from=0, to =1, by = .2)) 
Create a quintile for titanic explaining the Age variable 
titanic_w1_c50 <- C5.0(Survived~.,titanic) 
Build a cla...
- Examen
- • 11 páginas's •
-
Athabasca University•COMP 682 Data Mining
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P(E) is assumed to be the same for all ___ ______ (Naive Bayes) 
Naive Bayes Pros 
- Despite strict independence assumptions, performs surprisingly well for classification on real-world tasks 
- Natural and incremental learner. Needs not reprocess all past training examples when new data arrive 
- Fast, efficient, and effective 
quantile(titanic$Age, seq(from=0, to =1, by = .2)) 
Create a quintile for titanic explaining the Age variable 
titanic_w1_c50 <- C5.0(Survived~.,titanic) 
Build a cla...
1 
Precision (a or positive) 
or 
Positive Predictive Value (PPV) 
TP/(TP+FP) 
 
Positive Predictive Value (PPV) = TP/(TP+FP) 
 
2 
Precision (b or negative) 
or 
Negative Predictive Value (NPV) 
TN/(TN+FN) 
 
Negative Predictive Value (NPV) = TN/(TN+FN) 
 
3 
Recall (a or positive) = True Positive Rate (a) or TPR(a) 
Sensitivity =TP/(TP+FN) 
 
4 
Recall (b or negative) = True Negative Rate (b) 
Specificity = TN/(TN+FP) 
 
5 
F-MEASURE OR F-SCORE 
3 items (2 bullet points and the formula) 
1...
- Examen
- • 27 páginas's •
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Athabasca University•COMP 682 Data Mining
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1 
Precision (a or positive) 
or 
Positive Predictive Value (PPV) 
TP/(TP+FP) 
 
Positive Predictive Value (PPV) = TP/(TP+FP) 
 
2 
Precision (b or negative) 
or 
Negative Predictive Value (NPV) 
TN/(TN+FN) 
 
Negative Predictive Value (NPV) = TN/(TN+FN) 
 
3 
Recall (a or positive) = True Positive Rate (a) or TPR(a) 
Sensitivity =TP/(TP+FN) 
 
4 
Recall (b or negative) = True Negative Rate (b) 
Specificity = TN/(TN+FP) 
 
5 
F-MEASURE OR F-SCORE 
3 items (2 bullet points and the formula) 
1...
1 
What types of inputs do each of the 3 types of decision trees take? 
ID3: categorical only 
C4.5: numeric and categorical 
CART: numeric and categorical 
 
2 
How do each of the 3 types of decision trees split? 
ID3: max information gain and minimum entropy 
C4.5: information gain and gain ratio 
CART: Gini index and twoing creation 
 
3 
How do each of the 3 types of decision trees handle missing data? 
ID3: does not 
C4.5: omits from calculations 
CART: surrogate splits 
 
4 
What is CBA? 
...
- Examen
- • 5 páginas's •
-
Athabasca University•COMP682
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Preparando tu documento...
1 
What types of inputs do each of the 3 types of decision trees take? 
ID3: categorical only 
C4.5: numeric and categorical 
CART: numeric and categorical 
 
2 
How do each of the 3 types of decision trees split? 
ID3: max information gain and minimum entropy 
C4.5: information gain and gain ratio 
CART: Gini index and twoing creation 
 
3 
How do each of the 3 types of decision trees handle missing data? 
ID3: does not 
C4.5: omits from calculations 
CART: surrogate splits 
 
4 
What is CBA? 
...