Georgia Institute Of TechnologyISYE 6501Lecture Notes ISYE 6501 Midterm 1.VERIFIED NOTES

Document Content and Description Below

# Week 1 Introduction To Analytics Modeling - GTX ISYE 6501 - Introduction to Analytics Modeling answer important types of questions: what happened? = descriptive what is going to happen? = pre... dictive what actions are best? = prescriptive how do we create value with data? when can analytics answer these questions? Modeling: taking a real life situation and expressing it in math analyze in math and turn it into a solution best ways to learn: ask questions, discuss answers Course Structure: - knowledge building - experience building based on knowledge built in part 1 Knowledge Building: Models - learn all the models Cross Cutting - data prep, output quality, missing data will include mathematical intuition but keep it agile all developed with situations and examples basic mathematical detail Experience Based: case studies practice using models practice using models using the commonly used analytics softwares make sure you learn key basic concepts link material with real analytic questions develop learning beyond the videos learn to use software without being told exactly what to do Summary: knowledge building and then experience What is Modeling? - real life situation described in math - analyze the math - turn math analysis back to real-life solution the mathematical description of the problem is the model all the detail involved in modeling is 'the model' Introduction to Classification classification = putting things into categories put into groups of 'yes' and 'no' many analytic questions need to bin answers into a group based on the past examples we can use classification models to sort these items into these groups we can also have multiple classification groups - not just 'yes' or 'no' we need data to get these answers! we can infer and model from the data to classify a new point into the correct group! credit score and income example: scatterplot if repaid - green if defaulted - red these points could have an entire set of features associated with it we can draw a decision line between the points and sort them based on our decision line there are many lines! how do we know the 'right' line they could all separate the groups evenly! Choosing a Classifier what are the trade-offs in building classification models? we want to put things into categories! should we give someone a loan? we draw a line to sort groups into classification groups... what is the right line to draw? which one should we chose - the line that it further from making mistakes! we might not have all the data - we want find the line that is not close to make misclassifications what if it is impossible to avoid making classification mistakes... i.e. no line to separate between points? we need a 'soft' classifier rather than a 'hard' classifier we need as good as separation as possible - minimize the number of misclassified points we want to trade off between actual mistakes and 'near' mistakes not all mistakes are equal! the best separator - the most costly one type of decision is the further we shift our line away from this group! we can set a high classifier in order to limit cost of classification errors we can use the same idea for 'soft' classification also! we can tell from our decision line which variable is important to the classifier based on the scatterplot between the two variables horizontal line = the classifier only takes the vertical access into account vertical line = the classifier only takes the horizontal axis into account [Show More]

Last updated: 1 year ago

Preview 1 out of 40 pages