Feature Engineering Studio October 7, 2013. Welcome to Bring Me a Rock Day 2.

Slides:



Advertisements
Similar presentations
Welcome to your computer class!
Advertisements

Advanced Methods and Analysis for the Learning and Social Sciences PSY505 Spring term, 2012 January 23, 2012.
My Favorite Story.
Feature Engineering Studio January 21, Welcome to Feature Engineering Studio Design studio-style course teaching how to distill and engineer features.
© HRS Ltd, In NZ phone , A 6-minute slide show to illustrate the main features of Mathcad documents are used for engineering.
Special Topics in Educational Data Mining HUDK5199 Spring term, 2013 February 18, 2013.
Calendar Browser is a groupware used for booking all kinds of resources within an organization. Calendar Browser is installed on a file server and in a.
Section 4.2 Fitting Curves and Surfaces by Least Squares.
REPETITION (loops or iteration) Schneider: Sec. 6.1 & 6.3.
Regression in EXCEL r2 SSE b0 b1 SST.
Graphing the Set of All Solutions ~adapted from walch education.
Algebra 1 Ch 4.2 – Graphing Linear Equations. Objective Students will graph linear equations using a table. Students will graph linear equations using.
Excel Modeling Non Linear Regression Anchored By: Renu Rao Kaveh Saba.
TODAY IN ALGEBRA…  WARM UP: Determining whether an ordered pair is a solution and graphing linear equations  Learning Goal: 6.7 You will graph linear.
Naming Polygons Recognize any of these shapes? By: Keith Winberry.
Feature Engineering Studio February 23, Let’s start by discussing the HW.
Any questions on the Section 6.2 homework?. Please CLOSE YOUR LAPTOPS, and turn off and put away your cell phones, and get out your note- taking materials.
Solving Equations with Variables on Both Sides
Objective - To graph linear equations using x-y charts. One Variable Equations Two Variable Equations 2x - 3 = x = 14 x = 7 One Solution.
Mixed-level English classrooms What my paper is about: Basically my paper is about confirming with my research that the use of technology in the classroom.
Feature Engineering Week 3 Video 3. Feature Engineering.
Classifiers, Part 1 Week 1, video 3:. Prediction  Develop a model which can infer a single aspect of the data (predicted variable) from some combination.
CSC 395 – Software Engineering Lecture 34: Post-delivery Maintenance -or- What’s Worse than Being a Code Monkey?
Warm ups (HW will get stamped tomorrow) Complete the given ordered pairs of the following equation: y = 4x + 2 (-3,?) (?,0) (4,?) (?,5)
TODAY IN ALGEBRA…  Warm Up: Graphing Linear Equations and solving for y.  Learning Goal: 7.1 You will solve systems on linear equations by Graphing 
Feature Engineering Studio March 30, Iterative Feature Refinement.
Core Methods in Educational Data Mining HUDK4050 Fall 2014.
Advanced Methods and Analysis for the Learning and Social Sciences PSY505 Spring term, 2012 February 13, 2012.
Feature Engineering Studio September 23, Welcome to Mucking Around Day.
Assignment 2: remarks FIRST PART Please don’t make a division of labor so blatantly obvious! 1.1 recode - don't just delete everything that looks suspicious!
Feature Engineering Studio September 9, Welcome to Problem Proposal Day Rules for Presenters Rules for the Rest of the Class.
Feature Engineering Studio September 23, Let’s start by discussing the HW.
INFO 636 Software Engineering Process I Prof. Glenn Booker Week 8 – Reviews 1INFO636 Week 8.
Feature Engineering Studio October 14, Iterative Feature Refinement.
Is when you say something but it means something else.
Feature Engineering Studio March 1, Let’s start by discussing the HW.
Feature Engineering Studio September 30, Quick Note Please me for appointments rather than just showing up at my office – I’m always glad.
Experiment Design Overview Number of factors 1 2 k levels 2:min/max n - cat num regression models2k2k repl interactions & errors 2 k-p weak interactions.
You should be in groups of two You will first learn a different method on graphing systems of inequalities The lesson will lead you on to graphing with.
Special Topics in Educational Data Mining HUDK5199 Spring term, 2013 February 27, 2013.
Lit Circle Unit The How-to’s and the Whyfore’s. What is a Lit Circle A lit circle is a small group of people dedicated to one book and the complete mastery.
Core Methods in Educational Data Mining HUDK4050 Fall 2014.
By Godwin Alemoh. What is usability testing Usability testing: is the process of carrying out experiments to find out specific information about a design.
GRAPHING DATA S.Carbajal Grade 8 What You’ll Learn Today  Becoming familiar with and easily using graphing.  Different types of graphs: bar, line,
Feature Engineering Studio October 21, Feature Adaptation Presentations How many presenters do we have today? Five minutes per presentation N minutes.
Feature Engineering Studio September 9, Welcome to Feature Engineering Studio Design studio-style course teaching how to distill and engineer features.
6.5 Solving System of Linear Inequalities: VIDEOS equations/v/solving-linear-systems-by-graphing.
Feature Engineering Studio February 2, Welcome to Problem Proposal Day Rules for Presenters Rules for the Rest of the Class.
Feature Engineering Studio April 13, Friend Features Who managed to find a friend with relevant background expertise?
Tuesday, October 15, 2013 Do Now:. 3-1 Solving Systems of Equations by Graphing Objectives: 1)solve systems of linear equations by graphing 2) Determine.
First Grade Sight Words
TODAY IN ALGEBRA 2.0…  Review: Solving Linear Systems by Graphing  Learning Goal 1: 3.2 Solving Linear Systems by Substitution with one equation solved.
Dante Webbe.  Is a computer career for me?  To answer this question. I will create 3 algorithms to help determine whether or not a computer related.
Using Systems of Equations to Solve Problems A Write and/or solve a system of linear equations (including problem situations) using graphing,
Modeling. What is a Model?  A model is any representation of something; i.e. different ways to showing the same thing  Types of models: Verbal, Graphs,
Why Work In Groups ? Tahoma Jr. High 8 th Grade Science Maple Valley, WA.
Feature Engineering Studio October 7, Welcome to Bring Me Another Rock.
Correlation and Regression
Technology acts a direct substitute, with no functional improvement
Elements of Good Occupational Training Program Design
Good Morning Everyone!! Our Warm Up today is finishing the exam we began on Monday. You will have exactly 30 mins in class today before we need to move.
Core Methods in Educational Data Mining
Big Data, Education, and Society
Feature Engineering Studio
Welcome! August 15th, 2017 Tuesday
Feature Engineering Studio
What's Wrong with this Slide
Objectives Identify solutions of linear equations in two variables.
What's Wrong with this Slide
OSU Professional Development
Presentation transcript:

Feature Engineering Studio October 7, 2013

Welcome to Bring Me a Rock Day 2

But first… Excel Equation Solver

What it requires Parameters Goodness metric (typically SSR)

Linear Regression Example Look at prior variables – And how model prediction is created from predictor Create SSR variable

Linear Regression Example Hand-iterate on variables

Linear Regression Example Excel equation solver

BKT Example Go through functions

BKT Example Excel equation solver

BKT Example Excel equation solver – Constrain P(G) to under 0.3

BKT Example Excel equation solver – Try different solver algorithms

Questions? Comments?

GoogleRefine (now OpenRefine)

Functionality to make it easy to regroup and transform data – Find similar names – Connect names – Bin numerical data – Mathematical transforms showing resultant graphs – Text transforms and column creation

GoogleRefine (now OpenRefine) Functionality for finding anomalies/outliers

GoogleRefine (now OpenRefine) Functionality for automatically repeating the same process on a new data set *Really* nice for cases where you complete a complex process and want to repeat it

GoogleRefine (now OpenRefine) Functionality for connecting your data set to web services to get additional relevant info

GoogleRefine (now OpenRefine) Can load in and export common but hard-to- work-with data types – JSON and XML

GoogleRefine (now OpenRefine) Some videos you should watch later AWM Ba0 k

Questions? Comments?

Welcome to Bring Me a Rock Day 2

In birthdate order Each person should tell us about their favorite feature they created for Bring Me a Rock Day 2 Tell us what it was How you created it Your just-so story And was your just-so story correct

Next Tell us about anything cool you did in Excel or another program to create a feature

Too Hard? Were there any features that anyone kind of wanted to create, but it was too difficult? (or too much work?)

Better? Who here got better features (in terms of goodness metric) for Bring Me a Rock Day 2, than Bring Me a Rock Day 1?

Other Interesting Observations?

Assignment 5

Iterative Feature Refinement – Select three of the features you have created in previous assignments – These features should be “among the best” of the features you have previously created – For each of these three features, create at least five “close variants” of these features “time for last 3 actions” and “time for last 4 actions” are close variants “time for last 3 actions” and “total time between help requests and next action” are two separate features – Using the Excel Equation Solver is a substitute for creating five “close variants” – If you don’t use the excel equation solver As you create the close variants for each feature, don’t just make them all at once Make a variant Test whether it’s better than the previous variant (by goodness metric) – If it is, keep going in the same direction – If it isn’t, try doing the opposite or something else

Assignment 5 Write a report that discusses your process – I took feature N – I changed it from N to N* – The goodness changed from G to G* – Then I did…

Assignment 5 You don’t need to prepare a presentation But be ready to discuss your features in class

Next Classes 10/9 RapidMiner Practice Session – Bring your RapidMiner process to class with questions, on a laptop – We’ll learn together 10/14 Iterative Feature Refinement – Assignment 5 due

Upcoming Classes 10/16 No special session today 10/21 Feature Adaptation 10/23 Special Session on Building Prediction Models

Thank you!