SRM EXAM STUDYGUIDE LATEST UPDATE
K-Means Clustering - ANSWER -randomly assign a cluster to each observation.
This serves as the initial cluster assignments
-algorithm needs to be repeated for each K
-number of cluster must be pre-specified
Hierarchical Clustering - ANSWER -number of cluster does not need to be
pre-specified
-the algorithm only needs to be performed once for any number of clusters
-does not require random assignments
-results of clustering depends on the choice of number of clusters, dissimilarity
measure, and linkage
Principal Component Analysis - ANSWER -provides low-dimensional linear
surfaces that are closest to the observations
-the first principal component is the line in the p-dimensional space that is the
closest to the observations
-finds a lower dimension representation of a dataset that contains as much
variation as possible
-serves as a tool for data visualization
-uses all variables
Principal components - ANSWER -the proportional of variance explained by an
additional principal component decreases as more principal components are
added
-the cumulative proportion of variance explained increases as more principal
components are added
-the least number of principal component provides the best understanding of the
data
-a scree plot provides a method for determining the number of principal
components to use
,Which is most appropriate to model if a personal is hospitalized or not -
ANSWER - Binomial distribution
-logit link function ( restricts values to range 0 to 1) like binary classification
Alternative fitting procedure - ANSWER - removes irrelevant variables from
the predictor, thus leads to a simpler model
-results are easier to interpret
-accuracy will improve due to reduction in variance
Random Forest - ANSWER -if the number of predictors used at each split is
equal to the total number of available predictors, the result is the same as using
bagging
-when building a specific tree, a new subset of predictor variables is used at each
split
-improvement over bagging because the trees are decorrelated
Linear regression - ANSWER -considered inflexible because the number of
possible models is restricted to a certain form
-allows the analyst discretion regarding adding or removing variables
Lasso Regression - ANSWER -less flexible than a linear regression
-determines the subset of variables to use while linear regression allows the
analyst discretion regarding adding or removing variables
-performs variable selection, because it is possible for the coefficient estimates to
be exactly 0
- as tuning parameter increases, flexibility decrease
-irreducible error will remain constant
Bagging - ANSWER -provides additional flexibility
Flexibility & easy to interpret - ANSWER there is a trade off between flexibility
and easy to interpret
simple linear regression - ANSWER y = B0 + B1x+e
- if e= 0 then the confidence interval equals the prediction interval because the
, prediction interval includes the irreducible error
- the prediction interval is always at least as wide as the confidence interval
- the confidence interval quantifies the possible range for E(y I x)
Bias-Variance Tradeoff - ANSWER -bias refers to the error arising from the
assumption in the statistical learning tool
-variance refers to the error arising from the sensitivity of the training data set
-as model flexibility increases, squared bias decreases and variance increases
For K-nearest neighbors classifier, as K increase - ANSWER - squared bias
increases
-variance decrease
-flexibility decreases
regression problems - ANSWER problems with a quantitative response
classification problems - ANSWER problems with a qualitative response
supervised problems - ANSWER -problems with a response
-Boosting
-K-nearest neighbors
-Regression tree
-logistic regression
-ridge regression
unsupervised problems - ANSWER -problems without a clear response
-cluster analysis
-K-means clustering
Best Model - ANSWER Model with the lowest MSE
For an statistical learning method, as flexibility increase - ANSWER -the
interpretability decreases
-the training MSE decreases
K-Means Clustering - ANSWER -randomly assign a cluster to each observation.
This serves as the initial cluster assignments
-algorithm needs to be repeated for each K
-number of cluster must be pre-specified
Hierarchical Clustering - ANSWER -number of cluster does not need to be
pre-specified
-the algorithm only needs to be performed once for any number of clusters
-does not require random assignments
-results of clustering depends on the choice of number of clusters, dissimilarity
measure, and linkage
Principal Component Analysis - ANSWER -provides low-dimensional linear
surfaces that are closest to the observations
-the first principal component is the line in the p-dimensional space that is the
closest to the observations
-finds a lower dimension representation of a dataset that contains as much
variation as possible
-serves as a tool for data visualization
-uses all variables
Principal components - ANSWER -the proportional of variance explained by an
additional principal component decreases as more principal components are
added
-the cumulative proportion of variance explained increases as more principal
components are added
-the least number of principal component provides the best understanding of the
data
-a scree plot provides a method for determining the number of principal
components to use
,Which is most appropriate to model if a personal is hospitalized or not -
ANSWER - Binomial distribution
-logit link function ( restricts values to range 0 to 1) like binary classification
Alternative fitting procedure - ANSWER - removes irrelevant variables from
the predictor, thus leads to a simpler model
-results are easier to interpret
-accuracy will improve due to reduction in variance
Random Forest - ANSWER -if the number of predictors used at each split is
equal to the total number of available predictors, the result is the same as using
bagging
-when building a specific tree, a new subset of predictor variables is used at each
split
-improvement over bagging because the trees are decorrelated
Linear regression - ANSWER -considered inflexible because the number of
possible models is restricted to a certain form
-allows the analyst discretion regarding adding or removing variables
Lasso Regression - ANSWER -less flexible than a linear regression
-determines the subset of variables to use while linear regression allows the
analyst discretion regarding adding or removing variables
-performs variable selection, because it is possible for the coefficient estimates to
be exactly 0
- as tuning parameter increases, flexibility decrease
-irreducible error will remain constant
Bagging - ANSWER -provides additional flexibility
Flexibility & easy to interpret - ANSWER there is a trade off between flexibility
and easy to interpret
simple linear regression - ANSWER y = B0 + B1x+e
- if e= 0 then the confidence interval equals the prediction interval because the
, prediction interval includes the irreducible error
- the prediction interval is always at least as wide as the confidence interval
- the confidence interval quantifies the possible range for E(y I x)
Bias-Variance Tradeoff - ANSWER -bias refers to the error arising from the
assumption in the statistical learning tool
-variance refers to the error arising from the sensitivity of the training data set
-as model flexibility increases, squared bias decreases and variance increases
For K-nearest neighbors classifier, as K increase - ANSWER - squared bias
increases
-variance decrease
-flexibility decreases
regression problems - ANSWER problems with a quantitative response
classification problems - ANSWER problems with a qualitative response
supervised problems - ANSWER -problems with a response
-Boosting
-K-nearest neighbors
-Regression tree
-logistic regression
-ridge regression
unsupervised problems - ANSWER -problems without a clear response
-cluster analysis
-K-means clustering
Best Model - ANSWER Model with the lowest MSE
For an statistical learning method, as flexibility increase - ANSWER -the
interpretability decreases
-the training MSE decreases
