bcor 2205 final exam questions with correct answers

Artificial Intelligence - Answer- Machines that can perform tasks that are characteristic of human intelligence. Machine Learning - Answer- 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 - Answer- The variable we are trying to predict and gain insights about Features - Answer- Can be thought of as the independent variables we will use to predict the target supervised ML - Answer- Data scientist tells the machine what it wants it learn (identifies target) Unsupervised ML - Answer- Up to the machine to decide what it wants to learn Auto Machine Learning - Answer- the process of automating machine learning Exploratory data analysis - Answer- The process of examining the descriptive statistics for all features as well as their relationship with the target variable Feature engineering - Answer- Cleaning data, combining features, splitting features into multiple features, handling missing values, and dealing with text, etc. Algorithm selection and hyper-parameter tuning - Answer- Keeping up with the "dizzying number" of available algorithms and their quadrillions of parameter combinations Model diagnostics - Answer- Evaluation and ranking of top models the machine learning life cycle - Answer- define project objectives - acquire and explore data - model data - interpret and communicate - implement, document, and maintain define project objectives (1) - Answer- 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) - Answer- find appropriate data, merge data into single table, conduct exploratory data analysis, find and remove any target leakage, feature engineering model data (3) - Answer- variable selection, build candidate models, model validation and selection interpret and communicate (4) - Answer- interpret model, communicate model insights implement, document, and maintain (5) - Answer- set up batch or AP prediction system, document modeling process for reproducibility, create model monitoring and maintenance plan ·8 criteria of auto ML Excellence: - Answer- 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 - Answer- directly from Flat File Index - Answer- common way to talk about feature (i.e. feature #34) green bars - Answer- -indicated the relative importance of a particular feature when examined against the target independently of all other features. Var Type - Answer- Boolean, Categorical, Numeric, Text --Check that they match your expectations Unique - Answer- Number of unique values Missing - Answer- Number of missing values Descriptive Stats - Answer- Mean, Stddev, Median, Min, Max (Where applicable) ACE Scores - Answer- alternating conditional expectations, Very loosely a correlation for non-linear Boolean Variables - Answer- True or yes = 1 and false or no = 0 (binary) Text Variable - Answer- a specific diagnosis and how many times we see it Training Set - Answer- a subsection of a dataset from which the machine learning algorithm uncovers or "learns" relationships between the features and the target variable.