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Examen

COMP 682 Data Mining Final Exam 2026

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Publié le
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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$Age, seq(from=0, to =1, by = .2)) Create a quintile for titanic explaining the Age variable titanic_w1_c50 <- C5.0(Survived~.,titanic) Build a classification model using C5.0, titanic data, with the target variable of Survived. Titanic is the training set and the entire set is used. plot(titanic_w1_c50) plot the titanic_w1_c50 summary(titanic_w1_c50) Find the tree model of titanic_w1_c50 expressed in rules, the contingency matrix, and error rate titanic_w2_c50 <- C5.0(titanic[c(-1,-8)],titanic$Survived) Build a classification model using C5.0, titanic data, with the target variable of Survived, but remove variables 1 and 8 titanicf <- subset(titanic, Sex == "female") subset titanic dataframe to only include females table(titanicm$Survived, titanicm$Embarked) create a table to compare survived and embarked variables in the titanicm dataframe predict() applies a model (1st element) to a testing data set (2nd element) predicted_survived_w1 <- predict(titanic_w1_c50, titanic) Apply the titanic C50 model to the titanic testing data set. Assign it to the variable predicted_survived_w1 mmetric generates a confusion matrix (3rd element : "CONF") based on the true target variable (1st element) and the predicted target variable (2nd element) mmetric(true target, predicted target, metric types) mmetric syntax ACC, TPR, PRECISION, F1 Main mmetric metric types inTrain <- createDataPartition(titanic$Survived, p=0.5, list=FALSE) partition the titanic data set for 50% training with the target variable "Survived" titanicTrain <- titanic[inTrain,] titanicTest <- titanic[-inTrain,] Assign the rows in titanic indexed by inTrain to create a training set and use all other rows to create a testing set titanic_w1_nb <- naiveBayes(Survived~.,titanic) Build a naive bayes model using the titanic data set with Survived as the target variable on the entire data set. conditional probability

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Publié le
1 janvier 2026
Nombre de pages
11
Écrit en
2025/2026
Type
Examen
Contient
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COMP682


Clustering
The process of making a group of abstract objects into classes of similar
objects. Points to Remember. First partition the set, then apply labels to each
partition.
Clustering (Identify)
What type of data mining task?:
Several sets of companies. Each of these sets includes companies that are
similar to each other. Companies belonging to different sets are dissimilar to
one another.
Association Rule Mining (Identify)
What type of data mining task?:
Purchases of (A) imply simultaneous purchases of (B)
Association Rule Mining
A very popular DM method in business
Finds interesting relationships (affinities) between variables (items or events)
Classification (Identify)
What type of data mining task?:
A model that maps a patient's clinical history to positive or negative diagnosis
of a specific disease.
Classification
Assigns items in a collection to target categories or classes.
Regression or Numeric Prediction
A model that maps year-to-date world economic data to tomorrow's exchange
rate between two country's currencies.
Classification; Regression or Numeric Prediction
What are two supervised learning tasks?


COMP682

, COMP682


Association Rule Mining; Clustering
What are two unsupervised learning tasks?
Supervised Data Mining Requirements
The data has a target variable with well defined values; The values of the
target variable are available in training and testing data.
Supervised Learning
Category of data-mining techniques in which an algorithm learns how to
predict or classify an outcome variable of interest.
Unsupervised Learning
A type of model creation, derived from the field of machine learning, that
does not have a defined target variable.
gini index
A statistical formula that measures the amount of inequality in a society; its
scale ranges from 0 to 100, where 0 corresponds to perfect equality and 100
to perfect inequality
Entropy
A measure of disorder or randomness.
prop.table
converts a table object into a relative frequency table
summary
shows distributions of variables in a dataframe
Data Science, Business Analytics; Knowledge Discovery from Data
Terms used interchangeably with data mining
Descriptive, Predictive, Prescriptive
What types of analytics should use data mining?

COMP682
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