Download Se#ng up ML Problem

Se#ng up ML Problem Rao Vemuri UC Davis [email protected] Defini>on of ML •  A computer program is said to learn from –  experience E –  with respect to some class of tasks T –  and performance measure P, –  if its performance at T, as measured by P, improves with experience E. •  Example: T – Playing chess P – Percentage of games won E – Number of games played Three Different Tasks Quan>fying P •  Percentage of games won •  f (won, lost, draw) –  Win = 1 point –  Lost = -­‐1 point –  Draw = 0 points •  Amount of money won •  Championship >tles won Capturing Experience E: A Data Set 1 2 3 4 Outlook Temperature Humidity Windy Surfing Sunny Sunny Rainy Overcast Mild Hot Mild Cool True False False True Yes No No Yes Normal High Normal High A^ributes or Features 1 2 3 4 Outlook Temperature Humidity Windy Surfing? Sunny Sunny Rainy Overcast Mild Hot Mild Cool Normal High Normal High True False False True Yes No No Yes Features: Medical Example Blood Pres. Cholesterol 1 normal high Glucose high Heart AAack? True Yes 1 2 3 4 Outlook Temperature Humidity Windy Sunny Sunny Rainy Overcast Mild Hot Mild Cool Normal High Normal High True False False True Yes No No Yes Nota>on: Instance 1 2 3 4 Outlook Temperature Hunidity Windy Surfing Sunny Sunny Rainy Overcast Mild Hot Mild Cool True False False True Yes No No Yes Normal High Normal High Note on Features •  Each instance is described by the same set of features •  The features may be –  con>nuous (eg. Temperature) –  discrete (eg. Cost in $) –  Binary (eg. True/False) –  Categorical (eg. Red/Blue/Yellow) Nota>on: Data Set 1 2 3 4 Outlook Temperature Hunidity Windy Surfing Sunny Sunny Rainy Overcast Mild Hot Mild Cool True False False True Yes No No Yes Normal High Normal High Training & Test Sets •  Training Set: E (Experience) –  The last column has labels like YES or NO –  These labels are either given or inserted by an expert •  Test Set: t –  The last column has no labels –  Our job is find those labels Hypothesis •  A combina>on of a^ributes and our guess as to what the label should be for that combina>on –  If the (outlook = don’t care)^(Temp = cool)^(humidity=normal)^(Windy=Trues) is one possible hypothesis. –  For this hypothesis our machine should answer YES or NO Types of ML Algorithms •  Supervised: You are given labeled training data. Create a func>on that fits the data –  Classifica>on (looking for discrete categories) –  Regression (looking for a con>nuous func>on) •  Unsupervised: You are given unlabeled training data. Discover unknown, but useful, classes •  Reinforcement: The learner is not told which ac>ons to take. Discovers which ac>ons yield the best reward, in the long run. WEKA •  W(aikato) E(nvironment) for K(nowlegde) A(nalysis) •  Developed by the University of Waikato in New Zealand •  Machine Learning Tools and Techniques in Java •  Comprehensive suite of Java class libraries –  Implemented many state-­‐of-­‐the-­‐art machine learning and data mining algorithms •  h^p://www.cs.waikato.ac.nz/~ml/index.html WEKA Consists of… • 
• 
• 
• 
• 
Explorer Experimenter Knowledge Flow Simple Command Line Interface Java Interface Explorer •  Is WEKA’s main graphical user interface •  Weka package consists of –  Filters –  Classifiers –  Clusterers –  Associa>ons –  A^ribute Selec>on –  Visualiza>on tool Pre-­‐Processing •  Data loaded from URL or DB •  Preprocessing rou>nes in WEKA are called ‘filters’ – –  MergeA*ributeValuesFilter –  NominalToBinaryFilter –  Discre:seFilter –  ReplaceMissingValuesFilter Homework Assignment 1 •  Search for WEKA on the Web and write –  (a) 4 short sentences about what the best features of WEKA are. –  (b) One sentence on where WEKA is useful Assignment Due: A week from today (Sep 13). Write your answers in English and submit on one sheet of paper.