SOA Exam PA with all Correct & 100% Verified Answers |
Actual Complete Update |Already Graded A+
Random Sampling ✔Correct Answer-all members of the population have an equal chance of
being selected
Stratified Sampling ✔Correct Answer-A type of probability sampling in which the population is
divided into groups with a common attribute and a random sample is chosen within each group
Systematic Sampling ✔Correct Answer-Observations are drawn according to a set pattern and
there is no random mechanism controlling which observations are sampled
Model Selection using AIC ✔Correct Answer-All available data is used to train the potential
models. This method calculates how well it will fit the training data, with a preference for
models with fewer predictors. Because this method does not directly consider how well the
models fit new data, it does not give the user direct insight into how well the model generalizes
to unseen data.
Structured Data ✔Correct Answer-Data that (1) are typically numeric or categorical; (2) can be
organized and formatted in a way that is easy for computers to read, organize, and understand;
and (3) can be inserted into a database in a seamless fashion.
Unstructured data ✔Correct Answer-Nonnumeric information that is typically formatted in a
way that is meant for human eyes and not easily understood by computers
Univariate data exploration in R ✔Correct Answer-Numeric Variables: Use summary() function
or histogram/boxplots
Categorical Variables: Use table() function or bar charts (could use normal frequency counts or
proportions)
What to do with a right-skewed target variable ✔Correct Answer-Apply a monotone concave
function to shrink the outlier values and symmetrize the overall distribution while preserving
the ranks of the observed values of the variable. Transformations include:
- Log transformation - can be applied only if the variable of interest is strictly positive (not = 0!)
- Square-root transformation - can be applied to non-negative variables (can equal 0)
In general, the log transformation does a better job of remedying the right skewness of a
variable, but it may overdo things and make the transformed variable left-skewed.
Bivariate data exploration ✔Correct Answer-Numeric vs Numeric: Visualize in scatterplot, look
at correlation table (use cor() function)
, Numeric vs Categorical: Visualize with split box plots
Categorical vs Categorical: Two way frequency table (use table() function with 2 variables) or
split bar charts
Target variable ✔Correct Answer-the variable we are interested in predicting
Explanatory variables ✔Correct Answer-the variables used to predict the target variable, also
known as predictors or features
Supervised learning problems ✔Correct Answer-there is a target variable guiding our analysis,
and our goal is to understand the relationship between the target variable and the predictors,
and/or make accurate predictions for the target based on the predictors. GLMs and decision
trees fit into this category
Unsupervised learning problems ✔Correct Answer-a target variable is absent and we are
interested in extracting relationships and structures between different variables in the data.
Principal components analysis and cluster analysis fit into this category
Model Selection using a training/test split ✔Correct Answer-Use a large percentage of the
data to train the potential models and a smaller percentage to test the models. Because the test
data was not used to train the models, it can be used to directly assess how well each model
makes predictions when presented with new data. The testing is only done once, though, and
some data will never be used to train the model/some will never be used to train the model.
Root mean squared error (RMSE) ✔Correct Answer-A common numeric predictive metric that
aggregates all of the prediction errors on the test set and provides an overall measure of
predictive accuracy. The smaller the value, the more predictive the model. The metric has the
same unit as the target variable, which makes it easy to interpret as the absolute value of a
typical prediction error.
Misclassification error rate ✔Correct Answer-A common categorical variable predictive metric
that calculates the proportion of misclassified observations on the test set. The smaller the rate,
the more predictive the classifier.
Model Selection using n-fold Cross validation ✔Correct Answer-Splits the dataset into a
training and test group to train the model and later test the model. The split is done n times on
the same data such that all the data is used at some point for both training and test models.
While this method takes longer to carry out, it reduces overfitting.
Hyperparameter ✔Correct Answer-A parameter whose value must be set by the researcher
before learning begins. It often plays a role either in the mathematical expression of the
objective function, or in the set of constraints defining the optimization problem.
Actual Complete Update |Already Graded A+
Random Sampling ✔Correct Answer-all members of the population have an equal chance of
being selected
Stratified Sampling ✔Correct Answer-A type of probability sampling in which the population is
divided into groups with a common attribute and a random sample is chosen within each group
Systematic Sampling ✔Correct Answer-Observations are drawn according to a set pattern and
there is no random mechanism controlling which observations are sampled
Model Selection using AIC ✔Correct Answer-All available data is used to train the potential
models. This method calculates how well it will fit the training data, with a preference for
models with fewer predictors. Because this method does not directly consider how well the
models fit new data, it does not give the user direct insight into how well the model generalizes
to unseen data.
Structured Data ✔Correct Answer-Data that (1) are typically numeric or categorical; (2) can be
organized and formatted in a way that is easy for computers to read, organize, and understand;
and (3) can be inserted into a database in a seamless fashion.
Unstructured data ✔Correct Answer-Nonnumeric information that is typically formatted in a
way that is meant for human eyes and not easily understood by computers
Univariate data exploration in R ✔Correct Answer-Numeric Variables: Use summary() function
or histogram/boxplots
Categorical Variables: Use table() function or bar charts (could use normal frequency counts or
proportions)
What to do with a right-skewed target variable ✔Correct Answer-Apply a monotone concave
function to shrink the outlier values and symmetrize the overall distribution while preserving
the ranks of the observed values of the variable. Transformations include:
- Log transformation - can be applied only if the variable of interest is strictly positive (not = 0!)
- Square-root transformation - can be applied to non-negative variables (can equal 0)
In general, the log transformation does a better job of remedying the right skewness of a
variable, but it may overdo things and make the transformed variable left-skewed.
Bivariate data exploration ✔Correct Answer-Numeric vs Numeric: Visualize in scatterplot, look
at correlation table (use cor() function)
, Numeric vs Categorical: Visualize with split box plots
Categorical vs Categorical: Two way frequency table (use table() function with 2 variables) or
split bar charts
Target variable ✔Correct Answer-the variable we are interested in predicting
Explanatory variables ✔Correct Answer-the variables used to predict the target variable, also
known as predictors or features
Supervised learning problems ✔Correct Answer-there is a target variable guiding our analysis,
and our goal is to understand the relationship between the target variable and the predictors,
and/or make accurate predictions for the target based on the predictors. GLMs and decision
trees fit into this category
Unsupervised learning problems ✔Correct Answer-a target variable is absent and we are
interested in extracting relationships and structures between different variables in the data.
Principal components analysis and cluster analysis fit into this category
Model Selection using a training/test split ✔Correct Answer-Use a large percentage of the
data to train the potential models and a smaller percentage to test the models. Because the test
data was not used to train the models, it can be used to directly assess how well each model
makes predictions when presented with new data. The testing is only done once, though, and
some data will never be used to train the model/some will never be used to train the model.
Root mean squared error (RMSE) ✔Correct Answer-A common numeric predictive metric that
aggregates all of the prediction errors on the test set and provides an overall measure of
predictive accuracy. The smaller the value, the more predictive the model. The metric has the
same unit as the target variable, which makes it easy to interpret as the absolute value of a
typical prediction error.
Misclassification error rate ✔Correct Answer-A common categorical variable predictive metric
that calculates the proportion of misclassified observations on the test set. The smaller the rate,
the more predictive the classifier.
Model Selection using n-fold Cross validation ✔Correct Answer-Splits the dataset into a
training and test group to train the model and later test the model. The split is done n times on
the same data such that all the data is used at some point for both training and test models.
While this method takes longer to carry out, it reduces overfitting.
Hyperparameter ✔Correct Answer-A parameter whose value must be set by the researcher
before learning begins. It often plays a role either in the mathematical expression of the
objective function, or in the set of constraints defining the optimization problem.
