Data Mining Final Exam Study
Guide – Practice Questions with
Verified Answers. GRADED A+.
Latest 2026/2027 Update
Differentiate between classification and other predictive techniques - Answer✔✔-
Classification is a type of predictive modeling technique used to classify or
categorize data into predefined classes or categories based on certain features or
attributes. It is a supervised learning technique where a model is trained on
labeled data to make predictions on new, unseen data.
Other predictive techniques include regression, clustering, and association rule
mining. Regression is used to predict a continuous numerical value or outcome,
while clustering is used to group similar objects together based on their features
or attributes. Association rule mining is used to discover associations or patterns
among a set of variables.
Differentiate between supervised and unsupervised learning methods - -
Answer✔✔-•Unsupervised Learning
•The computer is presented only with inputs (independent variables)
•The computer attempts to classify things based on similarity/dissimilarity
•Supervised Learning
,•The computer is presented with inputs (independent variables) and associated
labels indicating the class of the observation (dependent variable)
•The computer attempts to learn the rule that maps inputs to
each class •New data is classified based on the rule learned
by the computer
Identify the roles of training, validation, and test data sets in the model and
development and evaluation - - Answer✔✔-Training data is used to construct the
classification model
Validation data is used to fine tune the models, assess their performance, and
select the "best" model for a given phenomenon
Test data is used to estimate the accuracy/future performance of the selected
model
new/unseen data contains only inputs and the predicted outputs enable decision
makers to extract value from the data
Identify the steps of the Naïve Bayes algorithm - - Answer✔✔-Prepare the data by
converting it into numerical form.
Calculate the prior probabilities of each class.
Calculate the likelihood of each feature given the class.
Calculate the posterior probabilities using Bayes' theorem.
Make predictions based on the highest posterior probability.
Identify the requirements for Naïve Bayes models - - Answer✔✔-Naïve Bayes
models require a set of labeled training data and the assumption of conditional
independence between the features given the class. They also require the data to
,be in numerical form, as the algorithm works with probabilities and requires
numerical inputs.
relies on the assumption that predictors are statistically independent
Interpret the results of Naïve Bayes models - - Answer✔✔-Naïve Bayes models
output the probability of each class given the input features. The class with the
highest probability is the predicted class. The results can be interpreted as the
likelihood of the input belonging to each class based on the available evidence.
The output probabilities can also be used to calculate the expected utility or cost
of each decision based on the predicted class.
Identify the steps of the logistic regression algorithm - - Answer✔✔-Prepare the
data by converting it into numerical form and splitting it into training and test sets.
Initialize the model parameters (coefficients) randomly.
Calculate the probabilities of the output variable (binary) based on the input
features using the logistic function.
Calculate the cost function (negative log-likelihood) to measure the error between
the predicted probabilities and the actual labels.
Update the model parameters using gradient descent to minimize the cost
function.
Repeat steps 3-5 until convergence or a stopping criterion is met.
Make predictions on new, unseen data by applying the trained model to the input
features.
Identify the requirements for logistic regression models - - Answer✔✔-Logistic
regression models require a set of labeled training data and the assumption of a
linear relationship between the input features and the log-odds of the output
, variable. They also require the data to be in numerical form, as the algorithm
works with probabilities and requires numerical inputs. Additionally, logistic
regression assumes that the data follows a binomial distribution and that the
observations are independent.
Interpret the results of logistic regression models - - Answer✔✔-Logistic
regression models output the predicted probability of the output variable (binary)
given the input features. The model coefficients represent the strength and
direction of the relationship between each input feature and the log-odds of the
output variable. These coefficients can be used to calculate the odds ratio, which
represents the change in odds of the output variable given a one-unit increase in
the corresponding input feature. The model can also be evaluated using metrics
such as accuracy, precision, recall, and F1 score. The results can be interpreted as
the likelihood of the input belonging to the positive class based on the available
evidence.
Identify the steps of the CaRT/ID3 algorithm - - Answer✔✔-tree construction is
performed in a top-down, recursive, divide-and-conquer manner
The CaRT (Classification and Regression Trees) and ID3 (Iterative Dichotomiser 3)
algorithms are decision tree algorithms that involve the following steps:
1.Using your training data, select the best attribute to split on
2.Identify all possible values for that attribute
3.For each value, create a new child node
4.Allocate the observations to the appropriate child node
5.For each child node
•If the node is pure, STOP
•Else, recursively call the algorithm to split again
Guide – Practice Questions with
Verified Answers. GRADED A+.
Latest 2026/2027 Update
Differentiate between classification and other predictive techniques - Answer✔✔-
Classification is a type of predictive modeling technique used to classify or
categorize data into predefined classes or categories based on certain features or
attributes. It is a supervised learning technique where a model is trained on
labeled data to make predictions on new, unseen data.
Other predictive techniques include regression, clustering, and association rule
mining. Regression is used to predict a continuous numerical value or outcome,
while clustering is used to group similar objects together based on their features
or attributes. Association rule mining is used to discover associations or patterns
among a set of variables.
Differentiate between supervised and unsupervised learning methods - -
Answer✔✔-•Unsupervised Learning
•The computer is presented only with inputs (independent variables)
•The computer attempts to classify things based on similarity/dissimilarity
•Supervised Learning
,•The computer is presented with inputs (independent variables) and associated
labels indicating the class of the observation (dependent variable)
•The computer attempts to learn the rule that maps inputs to
each class •New data is classified based on the rule learned
by the computer
Identify the roles of training, validation, and test data sets in the model and
development and evaluation - - Answer✔✔-Training data is used to construct the
classification model
Validation data is used to fine tune the models, assess their performance, and
select the "best" model for a given phenomenon
Test data is used to estimate the accuracy/future performance of the selected
model
new/unseen data contains only inputs and the predicted outputs enable decision
makers to extract value from the data
Identify the steps of the Naïve Bayes algorithm - - Answer✔✔-Prepare the data by
converting it into numerical form.
Calculate the prior probabilities of each class.
Calculate the likelihood of each feature given the class.
Calculate the posterior probabilities using Bayes' theorem.
Make predictions based on the highest posterior probability.
Identify the requirements for Naïve Bayes models - - Answer✔✔-Naïve Bayes
models require a set of labeled training data and the assumption of conditional
independence between the features given the class. They also require the data to
,be in numerical form, as the algorithm works with probabilities and requires
numerical inputs.
relies on the assumption that predictors are statistically independent
Interpret the results of Naïve Bayes models - - Answer✔✔-Naïve Bayes models
output the probability of each class given the input features. The class with the
highest probability is the predicted class. The results can be interpreted as the
likelihood of the input belonging to each class based on the available evidence.
The output probabilities can also be used to calculate the expected utility or cost
of each decision based on the predicted class.
Identify the steps of the logistic regression algorithm - - Answer✔✔-Prepare the
data by converting it into numerical form and splitting it into training and test sets.
Initialize the model parameters (coefficients) randomly.
Calculate the probabilities of the output variable (binary) based on the input
features using the logistic function.
Calculate the cost function (negative log-likelihood) to measure the error between
the predicted probabilities and the actual labels.
Update the model parameters using gradient descent to minimize the cost
function.
Repeat steps 3-5 until convergence or a stopping criterion is met.
Make predictions on new, unseen data by applying the trained model to the input
features.
Identify the requirements for logistic regression models - - Answer✔✔-Logistic
regression models require a set of labeled training data and the assumption of a
linear relationship between the input features and the log-odds of the output
, variable. They also require the data to be in numerical form, as the algorithm
works with probabilities and requires numerical inputs. Additionally, logistic
regression assumes that the data follows a binomial distribution and that the
observations are independent.
Interpret the results of logistic regression models - - Answer✔✔-Logistic
regression models output the predicted probability of the output variable (binary)
given the input features. The model coefficients represent the strength and
direction of the relationship between each input feature and the log-odds of the
output variable. These coefficients can be used to calculate the odds ratio, which
represents the change in odds of the output variable given a one-unit increase in
the corresponding input feature. The model can also be evaluated using metrics
such as accuracy, precision, recall, and F1 score. The results can be interpreted as
the likelihood of the input belonging to the positive class based on the available
evidence.
Identify the steps of the CaRT/ID3 algorithm - - Answer✔✔-tree construction is
performed in a top-down, recursive, divide-and-conquer manner
The CaRT (Classification and Regression Trees) and ID3 (Iterative Dichotomiser 3)
algorithms are decision tree algorithms that involve the following steps:
1.Using your training data, select the best attribute to split on
2.Identify all possible values for that attribute
3.For each value, create a new child node
4.Allocate the observations to the appropriate child node
5.For each child node
•If the node is pure, STOP
•Else, recursively call the algorithm to split again
