BARBER PRACTICE STATE BOARD
PRACTICE EXAMINATION 2026
QUESTIONS WITH ANSWERS GRADED
A+
◍ What does SVM stand for?.
Answer: Support Vector Machine
◍ Is written text structured or unstructured?.
Answer: Unstructured
◍ Main reasons to limit number of factors in model.
Answer: Overfitting - when # of factors is close to or larger than # of data
points; Simplicity - simple models are better; reduce the number of
correlated variables; certain variables might be hard to collect data or
expensive; some variables are missing data or hard to use
◍ When we increase the sum of the square of the coefficients we....
Answer: Decrease the distance between the lines
◍ Why are simple models better?.
Answer: Less data is required, less chance of insignificant factors, easier to
interpret
◍ In SVM soft classifier we tradeoff between maximizing ___ and minimizing
___.
Answer: margin and errors
◍ Examples of factors that are illegal to use.
Answer: race, sex, religion, marital status, or any factors that are highly
correlated with forbidden ones
,◍ What is forward selection?.
Answer: Start with a model with no factors, at each step, find each best new
factor to add to model, and put it in if good enough (parameter of your
choice like p value<=0.15) improvement; when there is no factor that is
good enough or if we add enough factors, we stop; can remove any factors
at the end
◍ If lambda gets small what gets emphasized, large margin or minimizing
training error?,.
Answer: Minimizing errors.
◍ What is backward selection?.
Answer: Start with all factors, and at each step, we find worst factor, and
remove from model and we keep going until no factor bad enough to
remove or we reach the number of factors we want
◍ What is stepwise regression?.
Answer: it is a combination of forward selection and backward elimination.
We can either start with all factors or no factors and at each step we remove
or add a factor. As we go through the procedure after adding each new
factor and at the end we eliminate right away factors that no longer appear;
Allows model to adjust if factor we earlier thought we needed no longer
seems necessary thanks to new factors added
◍ What is a support vector?.
Answer: A point that holds up a shape.
◍ What is a greedy algorithm?.
Answer: At each step, it does the one thing that looks best without future
options are considered
◍ Does ...[⅔(a-1)+1/3(a+1)] move an SVM classifier up or down?.
Answer: Up
◍ What are potential metrics to determine whether to add or remove factors in
variable selection?.
Answer: p value, AIC, BIC, R^2
, ◍ How do you make errors more costly in a soft SVM classifier?.
Answer: include a multiplier for the point-error term.
◍ What are methods that are based on optimization models that make
decisions globally looking at all options at the same time when it comes to
variable selection?.
Answer: LASSO, Elastic Net, Ridge Regression (not variable selection)
◍ If an SVM coefficient is very close to zero....
Answer: that term is not very important to the classification.
◍ What is the difference between standardization and scaling?.
Answer: Scaling is bounded in range. Standardization is scaling to a normal
distribution. Standardization is the (value - factor mean) / (factor standard
deviation)
◍ LASSO Approach.
Answer: Adds constraint to standard regression equation: we want to
minimize the sum of squared errors, but also the sum of coefficients cannot
be too large; add threshold t - budget to use on coefficients; need to scale
data - uses absolute value of coefficients
◍ What is the 2-norm?.
Answer: Euclidean distance
◍ Factors to consider when picking the right value of t for LASSO approach?.
Answer: Number of variables and quality of model
◍ What is the 1-norm?.
Answer: The rectilinear (Manhattan) distance
◍ What is the infinity norm?.
Answer: The value of the largest dimension
◍ Measuring the quality of a model is called?.
Answer: Validation
◍ Elastic Net.
PRACTICE EXAMINATION 2026
QUESTIONS WITH ANSWERS GRADED
A+
◍ What does SVM stand for?.
Answer: Support Vector Machine
◍ Is written text structured or unstructured?.
Answer: Unstructured
◍ Main reasons to limit number of factors in model.
Answer: Overfitting - when # of factors is close to or larger than # of data
points; Simplicity - simple models are better; reduce the number of
correlated variables; certain variables might be hard to collect data or
expensive; some variables are missing data or hard to use
◍ When we increase the sum of the square of the coefficients we....
Answer: Decrease the distance between the lines
◍ Why are simple models better?.
Answer: Less data is required, less chance of insignificant factors, easier to
interpret
◍ In SVM soft classifier we tradeoff between maximizing ___ and minimizing
___.
Answer: margin and errors
◍ Examples of factors that are illegal to use.
Answer: race, sex, religion, marital status, or any factors that are highly
correlated with forbidden ones
,◍ What is forward selection?.
Answer: Start with a model with no factors, at each step, find each best new
factor to add to model, and put it in if good enough (parameter of your
choice like p value<=0.15) improvement; when there is no factor that is
good enough or if we add enough factors, we stop; can remove any factors
at the end
◍ If lambda gets small what gets emphasized, large margin or minimizing
training error?,.
Answer: Minimizing errors.
◍ What is backward selection?.
Answer: Start with all factors, and at each step, we find worst factor, and
remove from model and we keep going until no factor bad enough to
remove or we reach the number of factors we want
◍ What is stepwise regression?.
Answer: it is a combination of forward selection and backward elimination.
We can either start with all factors or no factors and at each step we remove
or add a factor. As we go through the procedure after adding each new
factor and at the end we eliminate right away factors that no longer appear;
Allows model to adjust if factor we earlier thought we needed no longer
seems necessary thanks to new factors added
◍ What is a support vector?.
Answer: A point that holds up a shape.
◍ What is a greedy algorithm?.
Answer: At each step, it does the one thing that looks best without future
options are considered
◍ Does ...[⅔(a-1)+1/3(a+1)] move an SVM classifier up or down?.
Answer: Up
◍ What are potential metrics to determine whether to add or remove factors in
variable selection?.
Answer: p value, AIC, BIC, R^2
, ◍ How do you make errors more costly in a soft SVM classifier?.
Answer: include a multiplier for the point-error term.
◍ What are methods that are based on optimization models that make
decisions globally looking at all options at the same time when it comes to
variable selection?.
Answer: LASSO, Elastic Net, Ridge Regression (not variable selection)
◍ If an SVM coefficient is very close to zero....
Answer: that term is not very important to the classification.
◍ What is the difference between standardization and scaling?.
Answer: Scaling is bounded in range. Standardization is scaling to a normal
distribution. Standardization is the (value - factor mean) / (factor standard
deviation)
◍ LASSO Approach.
Answer: Adds constraint to standard regression equation: we want to
minimize the sum of squared errors, but also the sum of coefficients cannot
be too large; add threshold t - budget to use on coefficients; need to scale
data - uses absolute value of coefficients
◍ What is the 2-norm?.
Answer: Euclidean distance
◍ Factors to consider when picking the right value of t for LASSO approach?.
Answer: Number of variables and quality of model
◍ What is the 1-norm?.
Answer: The rectilinear (Manhattan) distance
◍ What is the infinity norm?.
Answer: The value of the largest dimension
◍ Measuring the quality of a model is called?.
Answer: Validation
◍ Elastic Net.
