BCOR 2205 Final Exam with 100% correct answers| [Latest 2026/2027 Update] Questions & Answers | Grade A | 100% Correct
Artificial Intelligence ✔️Machines that can perform tasks that are characteristic of human intelligence.
Machine Learning ✔️Subset of AI: The practice of using algorithms to parse data, learn from it, and then decide or prediction about something in
the word
Target ✔️The variable we are trying to predict and gain insights about
Features ✔️Can be thought of as the independent variables we will use to predict the target
supervised ML ✔️Data scientist tells the machine what it wants it learn (identifies target)
Unsupervised ML ✔️Up to the machine to decide what it wants to learn
Auto Machine Learning ✔️the process of automating machine learning
Exploratory data analysis ✔️The process of examining the descriptive statistics for all features as well as their relationship with the target
variable
Feature engineering ✔️Cleaning data, combining features, splitting features into multiple features, handling missing values, and dealing with
text, etc.
Algorithm selection and hyper-parameter tuning ✔️Keeping up with the "dizzying number" of available algorithms and their quadrillions of
parameter combinations
Model diagnostics ✔️Evaluation and ranking of top models
the machine learning life cycle ✔️define project objectives -> acquire and explore data -> model data -> interpret and communicate ->
implement, document, and maintain
define project objectives (1) ✔️specify business problem, acquire subject matter expertise, define unit of analysis and prediction target,
prioritize modeling criteria, consider risks and success criteria, decide whether to continue
acquire and explore data (2) ✔️find appropriate data, merge data into single table, conduct exploratory data analysis, find and remove any
target leakage, feature engineering
model data (3) ✔️variable selection, build candidate models, model validation and selection
interpret and communicate (4) ✔️interpret model, communicate model insights
implement, document, and maintain (5) ✔️set up batch or AP prediction system, document modeling process for reproducibility, create model
monitoring and maintenance plan
·8 criteria of auto ML Excellence: ✔️o Accuracy
o Productivity
o Ease of use
o Understanding and learning
o Resource availability
o Process transparency: effects understanding and learning
o Generalizability across contexts
o Recommended actions
Feature Name ✔️directly from Flat File
Artificial Intelligence ✔️Machines that can perform tasks that are characteristic of human intelligence.
Machine Learning ✔️Subset of AI: The practice of using algorithms to parse data, learn from it, and then decide or prediction about something in
the word
Target ✔️The variable we are trying to predict and gain insights about
Features ✔️Can be thought of as the independent variables we will use to predict the target
supervised ML ✔️Data scientist tells the machine what it wants it learn (identifies target)
Unsupervised ML ✔️Up to the machine to decide what it wants to learn
Auto Machine Learning ✔️the process of automating machine learning
Exploratory data analysis ✔️The process of examining the descriptive statistics for all features as well as their relationship with the target
variable
Feature engineering ✔️Cleaning data, combining features, splitting features into multiple features, handling missing values, and dealing with
text, etc.
Algorithm selection and hyper-parameter tuning ✔️Keeping up with the "dizzying number" of available algorithms and their quadrillions of
parameter combinations
Model diagnostics ✔️Evaluation and ranking of top models
the machine learning life cycle ✔️define project objectives -> acquire and explore data -> model data -> interpret and communicate ->
implement, document, and maintain
define project objectives (1) ✔️specify business problem, acquire subject matter expertise, define unit of analysis and prediction target,
prioritize modeling criteria, consider risks and success criteria, decide whether to continue
acquire and explore data (2) ✔️find appropriate data, merge data into single table, conduct exploratory data analysis, find and remove any
target leakage, feature engineering
model data (3) ✔️variable selection, build candidate models, model validation and selection
interpret and communicate (4) ✔️interpret model, communicate model insights
implement, document, and maintain (5) ✔️set up batch or AP prediction system, document modeling process for reproducibility, create model
monitoring and maintenance plan
·8 criteria of auto ML Excellence: ✔️o Accuracy
o Productivity
o Ease of use
o Understanding and learning
o Resource availability
o Process transparency: effects understanding and learning
o Generalizability across contexts
o Recommended actions
Feature Name ✔️directly from Flat File
