CMSC320.pdf - Lecture 2 Nominal(categorical values have names classes states of things \u200bhave no order Ordinal values have names classes states of

CMSC320.pdf - Lecture 2 Nominal(categorical values have...

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Lecture 2: - Nominal (categorical)- values have names, classes, states of things; have no order - Ordinal - values have names, classes, states of things; have order - Interval - scale with fixed but arbitrary intervals - Ratio - can look at the ratio of two quantities; does not have linear relationship Lecture 3: - A web-based Application Programming Interface (API) like we’ll be using in this class is a contract between a server and a user stating: - “If you send me a specific request, I will return some information in a structured and documented format.” - Representational State Transfer (RESTful) APIs: - GET: perform query, return data - POST: create a new entry or object - PUT: update an existing entry or object - DELETE: delete an existing entry or object Lecture 4: - FATML - F airness- Fairness can be viewed as a measure of diversity in the combinatorial space of sensitive attributes, as opposed to the geometric space of features. - A ccountability- Accountability of a mechanism implies an obligation to report, explain, or justify algorithmic decision-making as well as mitigate any negative social impacts or potential harms. - T ransparency- understandable; more meaningful; more accessible; and more measurable Lecture 5+6: - Tidy Data - “A standard method of displaying a multivariate set of data is in the form of a data matrix in which rows correspond to sample individuals and columns to variables, so that the entry in the ith row and jth column gives the value of the jth variate as measured or observed on the ith individual.” - Each variable forms a column. - Each observation forms a row. - Each type of observational unit forms a table - Melting Data - (1).pdf - Lecture 8: - Best fit regression line - difference between the true Y-values and the estimated Y-values is minimized - Square the error - Imputation: replacing missing data with substituted values - Mean imputation: imputing the average from observed cases for all missing values of a variable
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- Hot-deck imputation: imputing a value from another subject, or “donor,” that is
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  • Spring '17
  • John P. Dickerson
  • Missing values

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