Barga Data Science lecture 3
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The document discusses a lecture on deriving knowledge from data at scale. It outlines topics that will be covered, including forecasting techniques, introducing the Weka data mining tool, decision trees, and doing hands-on exercises with decision trees in Weka. The lecture objectives are also listed, which are to gain familiarity with Weka, understand decision trees, and get experience applying decision trees in Weka if time permits.
![Deriving Knowledge from Data at Scale
[1] "ETS(M,Md,M)“ – Holt Winter, multiplicative error, multiplicative
damped trend, multiplicative seasonality,](https://image.slidesharecdn.com/bargauwdatasciencelecture3-160424211506/85/Barga-Data-Science-lecture-3-82-320.jpg)
![Deriving Knowledge from Data at Scale
Example
Humidity Wind
High Normal Strong Weak
S: [9+,5-] S: [9+,5-]
S: [3+,4-] S: [6+,1-] S: [6+,2-] S: [3+,3-]
E = 0.985 E = 0.592 E = 0.811 E = 1.0
E = 0.940 E = 0.940
Gain(S, Humidity)
= .940 - (7/14).985 - (7/14).592
= 0.151
Gain(S, Wind)
= .940 - (8/14).811 - (6/14)1.0
= 0.048](https://image.slidesharecdn.com/bargauwdatasciencelecture3-160424211506/85/Barga-Data-Science-lecture-3-139-320.jpg)
Barga Data Science lecture 3
- 1. Deriving Knowledge from Data at Scale
- 2. Deriving Knowledge from Data at Scale Before we begin tonight… developer edition
- 3. Deriving Knowledge from Data at Scale
- 4. Deriving Knowledge from Data at Scale Lecture 3 Outline • Opening Discussion • Forecasting, continued (2/2) • Introducing Weka • Decision Trees • Hands On, Decision Tree in Weka (might be a stretch…)
- 5. Deriving Knowledge from Data at Scale Lecture 3 Outline • Understand elements of a time series • Excel as a tool • Practical application • Familiar with time series manipulation techniques • Automatic time series procedures (homework) • Gain familiarity with Weka • Dive into Decision Trees, in Weka (time permitting) Learning Objectives
- 6. Deriving Knowledge from Data at Scale Lecture 3 Outline Follow Up
- 7. Deriving Knowledge from Data at Scale Lecture 3 Outline • Opening Discussion • Forecasting, continued (2/2) • Introducing Weka • Decision Trees • Hands On, Decision Tree in Weka (might be a stretch…)
- 8. Deriving Knowledge from Data at Scale What tools to use?
- 9. Deriving Knowledge from Data at Scale
- 10. Deriving Knowledge from Data at Scale
- 11. Deriving Knowledge from Data at Scale
- 12. Deriving Knowledge from Data at Scale • Weka – explorer… • KNIME – experimentation… Get proficient in at least two (2) tools…
- 13. Deriving Knowledge from Data at Scale http://www.cs.waikato.ac.nz/ml/weka/ we’ll use this in class…
- 14. Deriving Knowledge from Data at Scale
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- 18. Deriving Knowledge from Data at Scale Fixed and known period Rise and fall, not a fixed period Trend Smoothing Moving Average Exponential Model Linear Exponential Auto Regressive
- 19. Deriving Knowledge from Data at Scale In the multiplicative mode for time series modeling Time Series = Trend component * Seasonality * Irregular Let’s assume the cyclical component is 0…
- 20. Deriving Knowledge from Data at Scale forecast Year 5
- 21. Deriving Knowledge from Data at Scale
- 22. Deriving Knowledge from Data at Scale
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- 26. Deriving Knowledge from Data at Scale trend
- 27. Deriving Knowledge from Data at Scale Linear regression deseasonalized Analysis ToolPak
- 28. Deriving Knowledge from Data at Scale Create time step column
- 29. Deriving Knowledge from Data at Scale Create time step column Select Data Analysis option Deseasonalize data Time Step Labels OK
- 30. Deriving Knowledge from Data at Scale
- 31. Deriving Knowledge from Data at Scale =(Intercept (F4 to lock) + slope (F4 to lock) * time code for row) =(5.099 + .147 * 1) Copy all the way down to Y4 Q4
- 32. Deriving Knowledge from Data at Scale seasonality seasonality * trend = prediction
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- 34. Deriving Knowledge from Data at Scale seasonality trend
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- 38. Deriving Knowledge from Data at Scale
- 39. Deriving Knowledge from Data at Scale Running Example: Amazon Orders
- 40. Deriving Knowledge from Data at Scale - 2,000,000 4,000,000 6,000,000 8,000,000 10,000,000 12,000,000 14,000,000 16,000,000 8/17/04 8/17/05 8/17/06 8/17/07 8/17/08 8/17/09 8/17/10 8/17/11 8/17/12 8/17/13 8/17/14 DailyOrders
- 41. Deriving Knowledge from Data at Scale - 1,000,000 2,000,000 3,000,000 4,000,000 5,000,000 6,000,000 2/1/15 2/8/15 2/15/15 2/22/15 3/1/15 3/8/15 3/15/15 3/22/15 3/29/15 4/5/15 4/12/15 4/19/15 4/26/15
- 42. Deriving Knowledge from Data at Scale - 2,000,000 4,000,000 6,000,000 8,000,000 10,000,000 12,000,000 14,000,000 16,000,000 11/1/14 11/8/14 11/15/14 11/22/14 11/29/14 12/6/14 12/13/14 12/20/14 12/27/14 1/3/15 1/10/15 1/17/15 1/24/15 1/31/15 2/7/15 2/14/15 2/21/15 2/28/15 3/7/15 3/14/15 3/21/15 3/28/15 4/4/15 4/11/15 4/18/15 4/25/15 Cyber Monday Black Friday Christmas Eve Super Saturday
- 43. Deriving Knowledge from Data at Scale yt »a1yt-1 +a2yt-7 +a3yt-365 +et SSresidual = yt - a1yt-1 +a2yt-7 +a3yt-365( )éë ùû 2 t å
- 44. Deriving Knowledge from Data at Scale Uncover Missing Data Missing vs. Anomalous Data
- 45. Deriving Knowledge from Data at Scale - 2,000,000 4,000,000 6,000,000 8,000,000 10,000,000 12,000,000 14,000,000 16,000,000 8/17/04 8/17/05 8/17/06 8/17/07 8/17/08 8/17/09 8/17/10 8/17/11 8/17/12 8/17/13 8/17/14 DailyOrders No lag Test Train (fit parameters)
- 46. Deriving Knowledge from Data at Scale 1. Coefficient of Determination (R2) 2. Mean Absolute Error (MAE) SStotal = (yt - y)2 t å R2 =1- SSresidual SStotal 1 T yt - a1yt-1 +a2yt-7 +a3yt-365( ) t å
- 47. Deriving Knowledge from Data at Scale yt » 0.59yt-1 +0.25yt-7 +0.20yt-365 +31,252
- 48. Deriving Knowledge from Data at Scale yt yt - 0.59yt-1 +0.25yt-7 +0.20yt-365 +31,252( )
- 49. Deriving Knowledge from Data at Scale yt » 0.57yt-1 +0.27yt-7 +0.19yt-365+2,288,140´I(CyberMonday)+30,239
- 50. Deriving Knowledge from Data at Scale
- 51. Deriving Knowledge from Data at Scale 10 Minute Break…
- 52. Deriving Knowledge from Data at Scale thousands
- 53. Deriving Knowledge from Data at Scale Data Downloads Data Downloads
- 54. Deriving Knowledge from Data at Scale
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- 57. Deriving Knowledge from Data at Scale
- 58. Deriving Knowledge from Data at Scale • Naïve method • Mean method • Seasonal naïve method • Drift method
- 59. Deriving Knowledge from Data at Scale
- 60. Deriving Knowledge from Data at Scale But by now, you know this…
- 61. Deriving Knowledge from Data at Scale
- 62. Deriving Knowledge from Data at Scale nothing is forecastable until it is stable… (1) Mean constant Volatility constant
- 63. Deriving Knowledge from Data at Scale
- 64. Deriving Knowledge from Data at Scale Transformation: take differences (diff() function in R) Transformation: take logs or powers. Box-Cox family of transformations flexibly covers both: Y = (lambda*y + 1)^(1/lambda)
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- 71. Deriving Knowledge from Data at Scale
- 72. Deriving Knowledge from Data at Scale y(t) = y(t-1) + e
- 73. Deriving Knowledge from Data at Scale
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- 76. Deriving Knowledge from Data at Scale arima() stats forecast() Arima() auto.arima() forecast
- 77. Deriving Knowledge from Data at Scale
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- 80. Deriving Knowledge from Data at Scale
- 81. Deriving Knowledge from Data at Scale
- 82. Deriving Knowledge from Data at Scale [1] "ETS(M,Md,M)“ – Holt Winter, multiplicative error, multiplicative damped trend, multiplicative seasonality,
- 83. Deriving Knowledge from Data at Scale
- 84. Deriving Knowledge from Data at Scale
- 85. Deriving Knowledge from Data at Scale
- 86. Deriving Knowledge from Data at Scale Feb Mar Apr May Jun Jul 114,727,363 123,818,067 132,671,221 141,424,018 150,134,416 158,826,902
- 87. Deriving Knowledge from Data at Scale These are scale dependent, so OK if comparing forecasts on the same data set or same scale of data…
- 88. Deriving Knowledge from Data at Scale
- 89. Deriving Knowledge from Data at Scale rolling forecasting origin
- 90. Deriving Knowledge from Data at Scale
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- 95. Deriving Knowledge from Data at Scale R A Little R Time Series Book Python/Pandas Video1 Video2 Video3 statsmodels Texts
- 96. Deriving Knowledge from Data at Scale Out of Class Reading (2), optional but very helpful…
- 97. Deriving Knowledge from Data at Scale For this project you can use the beer data set, or analyze a dataset of interest to you. The objective is to give you a hands on opportunity to work with the R time series functionality, in particular ARIMA. You can find time series datasets at the Time Series Data Library, or you can fallback to use the beer data set.
- 98. Deriving Knowledge from Data at Scale See homework description for what to turn in…
- 99. Deriving Knowledge from Data at Scale 10 Minute Break…
- 100. Deriving Knowledge from Data at Scale http://www.cs.waikato.ac.nz/ml/weka/
- 101. Deriving Knowledge from Data at Scale http://weka.wikispaces.com/ARFF+(stable+version)
- 102. Deriving Knowledge from Data at Scale
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- 121. Deriving Knowledge from Data at Scale
- 122. Deriving Knowledge from Data at Scale
- 123. Deriving Knowledge from Data at Scale • Opening Discussion • Forecasting, continued (2/2) • Introducing Weka • Decision Trees • Hands On, Decision Tree in Weka (stretch goal)
- 124. Deriving Knowledge from Data at Scale evaluation http://www.20q.net/
- 125. Deriving Knowledge from Data at Scale • Classification • Regression • Clustering classification trees
- 126. Deriving Knowledge from Data at Scale overcast high normal falsetrue sunny rain No NoYes Yes Yes Outlook Humidity Windy Each node is a test on one attribute Possible attribute values of the node Leafs are the decisions
- 127. Deriving Knowledge from Data at Scale overcast high normal falsetrue sunny rain No NoYes Yes Yes Outlook Humidity Windy Each node is a test on one attribute Possible attribute values of the node Leafs are the decisions Sample size Your data gets smaller
- 128. Deriving Knowledge from Data at Scale
- 129. Deriving Knowledge from Data at Scale overcast high normal falsetrue sunny rain No NoYes Yes Yes Outlook Humidity Windy A new test example: (Outlook==rain) and (not Windy==false) Pass it on the tree -> Decision is yes.
- 130. Deriving Knowledge from Data at Scale overcast high normal falsetrue sunny rain No NoYes Yes Yes Outlook Humidity Windy (Outlook ==overcast) -> yes (Outlook==rain) and (not Windy==false) ->yes (Outlook==sunny) and (Humidity=normal) ->yes
- 131. Deriving Knowledge from Data at Scale • The goal is to have the resulting decision tree as small as possible (Occam’s Razor) • Finding the minimal decision tree consistent with the data is NP-hard • Recursive algorithm is a greedy heuristic search for a simple tree, but cannot guarantee optimality. • Select attributes that split the examples to sets that are relatively pure in one label; this way we are closer to a leaf node.
- 132. Deriving Knowledge from Data at Scale test test Overfitting
- 133. Deriving Knowledge from Data at Scale Which attribute should be used as the test? Intuitively, you would prefer the one that separates the training examples as much as possible, reduces the entropy…
- 134. Deriving Knowledge from Data at Scale + - - + + + - - + - + - + + - - + + + - - + - + - - + - - + - + - - + - + - + + - - + + - - - + - + - + + - - + + + - - + - + - + + - - + - + - - + + + - + - + + - + - + + + - - + - + - - + - + - - + - + - + - - - + - - - - + - - + - - - + + + + + + + + - - - - - - - - - - - - + + + + + + + + + + + + + + - - + - + - + - + + + - - - - - - - - - - - - + + + + + + - - - - - Highly Disorganized High Entropy Highly Organized Low Entropy
- 135. Deriving Knowledge from Data at Scale amount of uncertainty
- 136. Deriving Knowledge from Data at Scale 4 + 4 - 8 + 0 - The distribution is less uniform Entropy is lower The node is purer
- 137. Deriving Knowledge from Data at Scale (information before split) – (information after split)
- 138. Deriving Knowledge from Data at Scale provides most information about the class reduces class entropy most information gain
- 139. Deriving Knowledge from Data at Scale Example Humidity Wind High Normal Strong Weak S: [9+,5-] S: [9+,5-] S: [3+,4-] S: [6+,1-] S: [6+,2-] S: [3+,3-] E = 0.985 E = 0.592 E = 0.811 E = 1.0 E = 0.940 E = 0.940 Gain(S, Humidity) = .940 - (7/14).985 - (7/14).592 = 0.151 Gain(S, Wind) = .940 - (8/14).811 - (6/14)1.0 = 0.048
- 140. Deriving Knowledge from Data at Scale Hypothesis space search in TDIDT
- 141. Deriving Knowledge from Data at Scale
- 142. Deriving Knowledge from Data at Scale area with probably wrong predictions Overfitting: Example + + + + + + + - - - - - -- - -- - - - - + - - - - -
- 143. Deriving Knowledge from Data at Scale That’s all for tonight….