Business Analytics Exam queries
with answers graded A+
Unsupervised Learning - ANS✅✅-No outcome of interest in advance of the learning process
-The goal is to explore relationships among historical cases or data sets, therefore, no training is
needed
Supervised Learning - ANS✅✅-Output of interest
-The goal is to train the machine to correctly recognize or measure that output.
-Training is based on exposing the machine (the model) to large number of historical cases and their
output or results
Data Preparation - ANS✅✅When dealing with large volumes of data, best practice is to extract a
representative sample for analysis. A sample is representative if the analyst can make the same
conclusions from it as from the entire population of data. The sample of data must be large enough
to contain significant information, yet small enough to be manipulated quickly. Data mining
algorithms typically are more effective given more data.
Data Preparation 2 - ANS✅✅When obtaining a representative sample, it is generally bet to include
as many variables as possible in the sample. After exploring the data with descriptive statistics and
visitation, the analyst can eliminate variables that are not of interest. Data mining applications deal
with an abundance o data that simplifies the process of assessing the accuracy of data-based
estimates of variable effects.
Data Preparation 3 - ANS✅✅Overfitting occurs when the analyst builds a model that does a great
job of explaining the sample of data on which it is based, but fails to accurately predict outside the
sample data. We can use the abundance of data to guard against the potential for overfitting by
splitting the data set into different subsets for: The training (or construction) of candidate models.
The validation (or performance comparison) of candidate models. The testing (or assessment) of
future performance of a selected model.
Data Preparation 4 - ANS✅✅Statistic Holdout Method: Training set: Consists of data used to build
the candidate models. Validation set: The data set to which the promising subset of models is
applied to identify which model is the most accuracy at predicting observations that were not used
to build the model. Test set: The data set to which the final model should be applied to estimate this
model's effectiveness when applied to data that have not been used to build or select the model.
, Performance - ANS✅✅Evaluating the Classification of Categorical Outcomes: By counting the
classification errors on a sufficiently large validation set and/or test set that is representative of the
population, we will generate an accurate measure of the model's classification performance.
Classification confusion matrix: Displays a model's correct and incorrect classifications.
Performance Measures 2 - ANS✅✅Overall error rate: Percentage of misclassified observations
Performance 3 - ANS✅✅Evaluating the Classification of Categorial Outcomes (cont.): One minus
the overall error rate is often referred to as the accuracy of the model. While overall error rate
conveys an aggregate measure of misclassification, it counts as misclassifying an actual Class 0
observation as a Class 1 observation (a false positive) the same as misclassifying an actual Class 1
observation as a Class 0 observation (a false negative).
Performance Error (Life Chart) - ANS✅✅Evaluating the Classification of Categorical Outcomes
(cont): Cumulative lift chart: Compares the number of actual Class 1 observations identified if
considered in decreasing order of their estimated probability of being in Class 1 and compares this to
the number of actual Class 1 observations identified if randomly selected.
Performance Measures 4 - ANS✅✅Evaluating the Classification Outcome (cont.): The ability to
correctly predict Class 1 (positive) observations is commonly expressed as sensitivity, or recall. The
ability to correctly predict Class 0 (negative) observation is commonly expressed as specificity.
Performance Measures 5 - ANS✅✅Evaluating the Classification of Categorial Outcomes (cont.):
Precision is a measure that corresponds to the proportion of observations predicted to be Class 1 by
a classifier that are actually in Class 1. The F1 Score combines precision and sensitivity into a single
measure and is defined as:
Performance Measures 6 - ANS✅✅Evaluating the Classification of Categorial Outcomes (cont.):
The receiver operating characteristic (ROC) curve is an alternative graphical approach for displaying
the tradeoff between a classifier's ability to correctly identify Class 1 observations and its Class 0
error rate. In general, we can evaluate the quality of a classifier by computing the area under the
ROC curve, often referred to as the AUC. The greater the area under the ROC craver, i.e., the largest
the AUC, the better the classifier performs.
Logistic Regression - ANS✅✅Logistic regression attempts to classify a binary categorical outcome
(y = 0 or 1) as a linear function of explanatory variables. A linear regression model fails to
appropriately explain a categorical outcome variable.
with answers graded A+
Unsupervised Learning - ANS✅✅-No outcome of interest in advance of the learning process
-The goal is to explore relationships among historical cases or data sets, therefore, no training is
needed
Supervised Learning - ANS✅✅-Output of interest
-The goal is to train the machine to correctly recognize or measure that output.
-Training is based on exposing the machine (the model) to large number of historical cases and their
output or results
Data Preparation - ANS✅✅When dealing with large volumes of data, best practice is to extract a
representative sample for analysis. A sample is representative if the analyst can make the same
conclusions from it as from the entire population of data. The sample of data must be large enough
to contain significant information, yet small enough to be manipulated quickly. Data mining
algorithms typically are more effective given more data.
Data Preparation 2 - ANS✅✅When obtaining a representative sample, it is generally bet to include
as many variables as possible in the sample. After exploring the data with descriptive statistics and
visitation, the analyst can eliminate variables that are not of interest. Data mining applications deal
with an abundance o data that simplifies the process of assessing the accuracy of data-based
estimates of variable effects.
Data Preparation 3 - ANS✅✅Overfitting occurs when the analyst builds a model that does a great
job of explaining the sample of data on which it is based, but fails to accurately predict outside the
sample data. We can use the abundance of data to guard against the potential for overfitting by
splitting the data set into different subsets for: The training (or construction) of candidate models.
The validation (or performance comparison) of candidate models. The testing (or assessment) of
future performance of a selected model.
Data Preparation 4 - ANS✅✅Statistic Holdout Method: Training set: Consists of data used to build
the candidate models. Validation set: The data set to which the promising subset of models is
applied to identify which model is the most accuracy at predicting observations that were not used
to build the model. Test set: The data set to which the final model should be applied to estimate this
model's effectiveness when applied to data that have not been used to build or select the model.
, Performance - ANS✅✅Evaluating the Classification of Categorical Outcomes: By counting the
classification errors on a sufficiently large validation set and/or test set that is representative of the
population, we will generate an accurate measure of the model's classification performance.
Classification confusion matrix: Displays a model's correct and incorrect classifications.
Performance Measures 2 - ANS✅✅Overall error rate: Percentage of misclassified observations
Performance 3 - ANS✅✅Evaluating the Classification of Categorial Outcomes (cont.): One minus
the overall error rate is often referred to as the accuracy of the model. While overall error rate
conveys an aggregate measure of misclassification, it counts as misclassifying an actual Class 0
observation as a Class 1 observation (a false positive) the same as misclassifying an actual Class 1
observation as a Class 0 observation (a false negative).
Performance Error (Life Chart) - ANS✅✅Evaluating the Classification of Categorical Outcomes
(cont): Cumulative lift chart: Compares the number of actual Class 1 observations identified if
considered in decreasing order of their estimated probability of being in Class 1 and compares this to
the number of actual Class 1 observations identified if randomly selected.
Performance Measures 4 - ANS✅✅Evaluating the Classification Outcome (cont.): The ability to
correctly predict Class 1 (positive) observations is commonly expressed as sensitivity, or recall. The
ability to correctly predict Class 0 (negative) observation is commonly expressed as specificity.
Performance Measures 5 - ANS✅✅Evaluating the Classification of Categorial Outcomes (cont.):
Precision is a measure that corresponds to the proportion of observations predicted to be Class 1 by
a classifier that are actually in Class 1. The F1 Score combines precision and sensitivity into a single
measure and is defined as:
Performance Measures 6 - ANS✅✅Evaluating the Classification of Categorial Outcomes (cont.):
The receiver operating characteristic (ROC) curve is an alternative graphical approach for displaying
the tradeoff between a classifier's ability to correctly identify Class 1 observations and its Class 0
error rate. In general, we can evaluate the quality of a classifier by computing the area under the
ROC curve, often referred to as the AUC. The greater the area under the ROC craver, i.e., the largest
the AUC, the better the classifier performs.
Logistic Regression - ANS✅✅Logistic regression attempts to classify a binary categorical outcome
(y = 0 or 1) as a linear function of explanatory variables. A linear regression model fails to
appropriately explain a categorical outcome variable.
