AIC 300 CLAIMS IN AN EVOLVING WORLD
CONCLUDING REVIEW SHEET PRACTICE
SOLUTION BUNDLED FULLY VERIFIED
ONE HUNDRED PERCENT PASS
GUARANTEED
⩥ Classification.
Answer: Categorizing members of a dataset based on known
characteristics
⩥ Regression analysis.
Answer: A statistical technique that predicts a numerical value given
characteristics of each member of a dataset.
⩥ Association rule learning.
Answer: Examining data to discover new and interesting relationships.
From these relationships, algorithms are used to develop rules to apply
to new data. An insurer can explore data to find relationships among its
products purchased.
⩥ cluster analysis.
Answer: Using statistical methods, a computer program explores data to
find groups with common and previously unknown characteristics.
,⩥ Data mining.
Answer: The analysis of large amounts of data to find new relationships
and patterns that will assist in developing business solutions.
⩥ Algorithm.
Answer: An operational sequence used to solve mathematical problems
and to create computer programs.
⩥ Cross Industry Standard Process for Data Mining (CRISP-DM).
Answer: An accepted standard for the steps in any data mining process
used to provide business solutions.
⩥ Machine learning.
Answer: Artificial intelligence in which computers continually teach
themselves to make better decisions based on previous results and new
data.
⩥ Complex claim.
Answer: A claim that contains one or more characteristics that cause it to
cost more than the average claim.
⩥ Information gain.
Answer: A measure of the predictive power of one or more attributes.
, ⩥ Classification tree.
Answer: A supervised learning technique that uses a structure similar to
a tree to segment data according to known attributes to determine the
value of a categorical target variable.
⩥ Recursively.
Answer: Successively applying a model.
⩥ Root node.
Answer: The first node in a classification tree.
⩥ Combination of nodes.
Answer: A representation of data attributes in a classification tree.
⩥ Leaf node.
Answer: A terminal node of a classification tree that is used to classify
an instance based on its attributes.
⩥ Training data.
Answer: Data that is used to train a predictive model and that therefore
must have known values for the target variable of the model.
CONCLUDING REVIEW SHEET PRACTICE
SOLUTION BUNDLED FULLY VERIFIED
ONE HUNDRED PERCENT PASS
GUARANTEED
⩥ Classification.
Answer: Categorizing members of a dataset based on known
characteristics
⩥ Regression analysis.
Answer: A statistical technique that predicts a numerical value given
characteristics of each member of a dataset.
⩥ Association rule learning.
Answer: Examining data to discover new and interesting relationships.
From these relationships, algorithms are used to develop rules to apply
to new data. An insurer can explore data to find relationships among its
products purchased.
⩥ cluster analysis.
Answer: Using statistical methods, a computer program explores data to
find groups with common and previously unknown characteristics.
,⩥ Data mining.
Answer: The analysis of large amounts of data to find new relationships
and patterns that will assist in developing business solutions.
⩥ Algorithm.
Answer: An operational sequence used to solve mathematical problems
and to create computer programs.
⩥ Cross Industry Standard Process for Data Mining (CRISP-DM).
Answer: An accepted standard for the steps in any data mining process
used to provide business solutions.
⩥ Machine learning.
Answer: Artificial intelligence in which computers continually teach
themselves to make better decisions based on previous results and new
data.
⩥ Complex claim.
Answer: A claim that contains one or more characteristics that cause it to
cost more than the average claim.
⩥ Information gain.
Answer: A measure of the predictive power of one or more attributes.
, ⩥ Classification tree.
Answer: A supervised learning technique that uses a structure similar to
a tree to segment data according to known attributes to determine the
value of a categorical target variable.
⩥ Recursively.
Answer: Successively applying a model.
⩥ Root node.
Answer: The first node in a classification tree.
⩥ Combination of nodes.
Answer: A representation of data attributes in a classification tree.
⩥ Leaf node.
Answer: A terminal node of a classification tree that is used to classify
an instance based on its attributes.
⩥ Training data.
Answer: Data that is used to train a predictive model and that therefore
must have known values for the target variable of the model.
