Barga Data Science lecture 4

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Information about Barga Data Science lecture 4

Published on April 24, 2016

Author: rsbarga

Source: slideshare.net

1. Deriving Knowledge from Data at Scale

2. Deriving Knowledge from Data at Scale • Opening Discussion 30 minutes Review Discussion… • Hands-On with Decision Trees 30 minutes • Ensembles, Random Forests 60 minutes • Data Science Modelling 30 minutes Model performance evaluation… • Machine Learning Boot Camp ~60 minutes Clustering, k-Means…

3. Deriving Knowledge from Data at Scale • Optional Reading: Data Science Weekly (2) • Two Homework Assignments, due next Wednesday 1. One is described in the lecture notes 2. Two is uploaded to the class Catalyst site • Key Points to Understand, review and discuss 1. Ensembles, the techniques of Bagging and Boosting 2. Random Forests 3. Clustering, specifically K-Means Clustering What will your data science workflow be? (not having one is a fail…)

4. Deriving Knowledge from Data at Scale gender age smoker eye color male 19 yes green female 44 yes gray male 49 yes blue male 12 no brown female 37 no brown female 60 no brown male 44 no blue female 27 yes brown female 51 yes green female 81 yes gray male 22 yes brown male 29 no blue lung cancer no yes yes no no yes no no yes no no no male 77 yes gray male 19 yes green female 44 no gray ? ? ?

5. Deriving Knowledge from Data at Scale gender age smoker eye color male 19 yes green female 44 yes gray male 49 yes blue male 12 no brown female 37 no brown female 60 no brown male 44 no blue female 27 yes brown female 51 yes green female 81 yes gray male 22 yes brown male 29 no blue lung cancer no yes yes no no yes no no yes no no no male 77 yes gray male 19 yes green female 44 no gray ? ? ? Train ML Model

6. Deriving Knowledge from Data at Scale gender age smoker eye color male 19 yes green female 44 yes gray male 49 yes blue male 12 no brown female 37 no brown female 60 no brown male 44 no blue female 27 yes brown female 51 yes green female 81 yes gray male 22 yes brown male 29 no blue lung cancer no yes yes no no yes no no yes no no no male 77 yes gray male 19 yes green female 44 no gray yes no no Train ML Model

7. Deriving Knowledge from Data at Scale Define Objective Access and Understand the Data Pre-processing Feature and/or Target construction 1. Define the objective and quantify it with a metric – optionally with constraints, if any. This typically requires domain knowledge. 2. Collect and understand the data, deal with the vagaries and biases in the data acquisition (missing data, outliers due to errors in the data collection process, more sophisticated biases due to the data collection procedure etc 3. Frame the problem in terms of a machine learning problem – classification, regression, ranking, clustering, forecasting, outlier detection etc. – some combination of domain knowledge and ML knowledge is useful. 4. Transform the raw data into a “modeling dataset”, with features, weights, targets etc., which can be used for modeling. Feature construction can often be improved with domain knowledge. Target must be identical (or a very good proxy) of the quantitative metric identified step 1.

8. Deriving Knowledge from Data at Scale Feature selection Model training Model scoring Evaluation Train/ Test split 5. Train, test and evaluate, taking care to control bias/variance and ensure the metrics are reported with the right confidence intervals (cross-validation helps here), be vigilant against target leaks (which typically leads to unbelievably good test metrics) – this is the ML heavy step.

9. Deriving Knowledge from Data at Scale Data Science Workflow.pdf Develop your own for defining and evaluating project opportunities…

10. Deriving Knowledge from Data at Scale Example 1: Amazon, big spenders. Target of the competition was to predict customers who spend a lot of money among customers using past purchases. The data consisted of transaction data in different categories. But a winning model identified that ‘Free shipping = True’ was an excellent predictor. Leakage: “Free Shipping = True” was simultaneous with the sale, which is a no-no… We can only use data from beforehand to predict the future…

11. Deriving Knowledge from Data at Scale Example 2: Cancer patients plotted by Patient ID – what happened? What could you do to improve this?...

12. Deriving Knowledge from Data at Scale Winning competition on leakage is easier than building good models. But even if you don’t explicitly understand and game the leakage, your model will do it for you. Either way, leakage is a huge problem. • You need a strict temporal cutoff: remove all information just prior to the event of interest. • There has to be a timestamp on every entry and you need to keep it • The best practice is to start from scratch with clean, raw data after careful consideration • You need to know how the data was created! I (try to ) work only with data I pulled and prepared myself…

13. Deriving Knowledge from Data at Scale To avoid overfitting, we cross-validate and we cut down on the complexity of the model to begin with. Here’s a standard picture (although keep in mind we generally work in high dimensional space and don’t have a pretty picture to look at)

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15. Deriving Knowledge from Data at Scale The art in data science The science in data science some evaluation metric rigorous testing and experimentation to either validate or refute

16. Deriving Knowledge from Data at Scale Given We need to determine evaluation metrics

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21. Deriving Knowledge from Data at Scale 0.65 0.67 0.69 0.71 0.73 0.75 0.77 0.79 0 500 1000 1500 2000 2500 3000 A c c u r a c y # Training Records Accuracy on test data stabilizes above 1000 training samples

22. Deriving Knowledge from Data at Scale Review: Decision Tree 1. Automatically selects features 2. Able to handle large number of features 3. Numeric, nominal, missing 4. Easy to ensemble (Random Forrest, Boosted DT) 5. I can romance on DTs for hours …

23. Deriving Knowledge from Data at Scale Blood pressure Drug A Age Drug A Drug B Drug B high normal low ≤ 40 > 40 Assignment of drug to a patient

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28. Deriving Knowledge from Data at Scale + + ++ + + + + + + + +

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31. Deriving Knowledge from Data at Scale + + ++ + + + + + + + +

32. Deriving Knowledge from Data at Scale + + ++ + + + + + + + + pm=5/6 Once regions are chosen class probabilities are easy to calculate

33. Deriving Knowledge from Data at Scale Decision Trees & Weka

34. Deriving Knowledge from Data at Scale

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54. Deriving Knowledge from Data at Scale When completed, submit this assignment to the dropbox for homework Lecture 4

55. Deriving Knowledge from Data at Scale Your task for this assignment: Design a simple, low-cost sensor that can distinguish between red wine and white wine. Your sensor must correctly distinguish between red and white wine for at least 95% of the samples in a set of 6497 test samples of red and white wine. Your technology is capable of sensing the following wine attributes: - Fixed acidity - Free sulphur dioxide - Volatile acidity - Total sulphur dioxide - Citric acid - Sulphates - Residual sugar - pH - Chlorides - Alcohol - Density To keep your sensor cheap and simple, you need to sense as few of these attributes as possible to meet the 95% requirement. Question: Which attributes should your sensor be capable of measuring?

56. Deriving Knowledge from Data at Scale 1. Go to our class website, Lecture 4, and download the associated homework files 2. Read WineQuality.pdf. 3. Open the RedWhiteWine.arff file in Weka, and remove the quality attribute, which you will not need for this assignment. 4. Run J48 with default set-up to see what kind of percent correct classification results you get using all attributes. 5. Remove attributes to find the minimum number of attributes needed to meet the 95% correct classification requirement. Remove

57. Deriving Knowledge from Data at Scale Use these buttons to simplify the task of removing attributes You can use these buttons to simplify the task of removing attributes

58. Deriving Knowledge from Data at Scale (Paste a screenshot showing your minimum attribute set here)

59. Deriving Knowledge from Data at Scale (Paste a screenshot showing your results for your minimum attribute set here)

60. Deriving Knowledge from Data at Scale • Opening Discussion 30 minutes Review Discussion… • Ensembles, Random Forests 60 minutes • Data Science Modelling 30 minutes Model performance evaluation… • Machine Learning Boot Camp ~60 minutes Clustering, k-Means… • Close

61. Deriving Knowledge from Data at Scale bagging Decision trees

62. Deriving Knowledge from Data at Scale

63. Deriving Knowledge from Data at Scale

64. Deriving Knowledge from Data at Scale

65. Deriving Knowledge from Data at Scale

66. Deriving Knowledge from Data at Scale Diversity of Opinion Independence Decentralization Aggregation

67. Deriving Knowledge from Data at Scale Ensemble Classification

68. Deriving Knowledge from Data at Scale Given Method Goal

69. Deriving Knowledge from Data at Scale The basic idea: Randomly draw datasets with replacement from the training data, each sample the same size as the original training set

70. Deriving Knowledge from Data at Scale Training • Regression • Classification

71. Deriving Knowledge from Data at Scale

72. Deriving Knowledge from Data at Scale

73. Deriving Knowledge from Data at Scale

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76. Deriving Knowledge from Data at Scale

77. Deriving Knowledge from Data at Scale

78. Deriving Knowledge from Data at Scale Two examples of random decisions in RFs

79. Deriving Knowledge from Data at Scale

80. Deriving Knowledge from Data at Scale

81. Deriving Knowledge from Data at Scale Ellipsoid separation  Two categories, Two predictors Single tree decision boundary 100 bagged trees..

82. Deriving Knowledge from Data at Scale Random Forest Classifier NexamplesTraining Data M features

83. Deriving Knowledge from Data at Scale Random Forest Classifier Nexamples Create bootstrap samples from the training data ....… M features

84. Deriving Knowledge from Data at Scale Random Forest Classifier Nexamples Construct a decision tree ....… M features

85. Deriving Knowledge from Data at Scale Random Forest Classifier Nexamples ....… M features At each node in choosing the split feature choose only among m<M features

86. Deriving Knowledge from Data at Scale Random Forest Classifier Create decision tree from each bootstrap sample Nexamples ....… ....… M features

87. Deriving Knowledge from Data at Scale Random Forest Classifier Nexamples ....… ....… Take the majority vote M features

88. Deriving Knowledge from Data at Scale Random Forests

89. Deriving Knowledge from Data at Scale Consensus Independence Decentralization Aggregation

90. Deriving Knowledge from Data at Scale Diversity of Opinion private information Independence Decentralization Aggregation

91. Deriving Knowledge from Data at Scale

92. Deriving Knowledge from Data at Scale Decision Trees and Decision Forests A forest is an ensemble of trees. The trees are all slightly different from one another. terminal (leaf) node internal (split) node root node0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 A general tree structure Is top part blue? Is bottom part green? Is bottom part blue? A decision tree

93. Deriving Knowledge from Data at Scale Decision Forest Model: the randomness model 1) Bagging (randomizing the training set) The full training set The randomly sampled subset of training data made available for the tree t Forest training

94. Deriving Knowledge from Data at Scale Decision Forest Model: the randomness model The full set of all possible node test parameters For each node the set of randomly sampled features Randomness control parameter. For no randomness and maximum tree correlation. For max randomness and minimum tree correlation. 2) Randomized node optimization (RNO) Small value of ; little tree correlation. Large value of ; large tree correlation. The effect of Node weak learner Node test params Node training

95. Deriving Knowledge from Data at Scale Decision Forest Model: training and information gain Beforesplit Information gain Shannon’s entropy Node training (for categorical, non-parametric distributions) Split1Split2

96. Deriving Knowledge from Data at Scale Why we prune…

97. Deriving Knowledge from Data at Scale Classification Forest Training data in feature space ? ? ? Entropy of a discrete distribution with Classification tree training Obj. funct. for node j (information gain) Training node j Output is categorical Input data point Node weak learner Predictor model (class posterior) Model specialization for classification ( is feature response) (discrete set)

98. Deriving Knowledge from Data at Scale Classification Forest: the weak learner model Node weak learner Node test params Splitting data at node j Weak learner: axis aligned Weak learner: oriented line Weak learner: conic section Examples of weak learners Feature response for 2D example. With a generic line in homog. coordinates. Feature response for 2D example. With a matrix representing a conic. Feature response for 2D example. In general may select only a very small subset of features With or

99. Deriving Knowledge from Data at Scale Classification Forest: the prediction model What do we do at the leaf? leaf leaf leaf Prediction model: probabilistic

100. Deriving Knowledge from Data at Scale Classification Forest: the ensemble model Tree t=1 t=2 t=3 Forest output probability The ensemble model

101. Deriving Knowledge from Data at Scale Training different trees in the forest Testing different trees in the forest (2 videos in this page) Classification Forest: effect of the weak learner model Parameters: T=200, D=2, weak learner = aligned, leaf model = probabilistic • “Accuracy of prediction” • “Quality of confidence” • “Generalization” Three concepts to keep in mind: Training points

102. Deriving Knowledge from Data at Scale Classification Forest: with >2 classes Training different trees in the forest Testing different trees in the forest Parameters: T=200, D=3, weak learner = conic, leaf model = probabilistic (2 videos in this page) Training points

103. Deriving Knowledge from Data at Scale Classification Forest: effect of tree depth max tree depth, D overfittingunderfitting T=200, D=3, w. l. = conic T=200, D=6, w. l. = conic T=200, D=15, w. l. = conic Predictor model = prob.(3 videos in this page) Training points: 4-class mixed

104. Deriving Knowledge from Data at Scale Classification Forest: analysing generalization Parameters: T=200, D=13, w. l. = conic, predictor = prob. (3 videos in this page) Training points: 4-class spiral Training pts: 4-class spiral, large gaps Tr. pts: 4-class spiral, larger gapsTestingposteriors

105. Deriving Knowledge from Data at Scale Q

106. Deriving Knowledge from Data at Scale

107. Deriving Knowledge from Data at Scale 10 Minute Break…

108. Deriving Knowledge from Data at Scale • Opening Discussion 30 minutes Review Discussion… • Ensembles, Random Forests 60 minutes • Data Science Modelling 30 minutes Model performance evaluation… • Machine Learning Boot Camp ~60 minutes Clustering, k-Means… • Close

109. Deriving Knowledge from Data at Scale Data Acquisition Data Exploration Pre-processing Feature and Target construction Train/ Test split Feature selection Model training Model scoring Model scoring Evaluation Evaluation Compare metrics

110. Deriving Knowledge from Data at Scale Model Scoring (subject for today…)

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112. Deriving Knowledge from Data at Scale

113. Deriving Knowledge from Data at Scale 1 2 3 4 (k-1) k Train Test

114. Deriving Knowledge from Data at Scale • Class • Score

115. Deriving Knowledge from Data at Scale True Label Predicted Label Confusion matrix

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117. Deriving Knowledge from Data at Scale

118. Deriving Knowledge from Data at Scale Performance Metrics Percent Reduction in Error • 80% accuracy = 20% error • Suppose learning increases accuracy from 80% to 90% error reduced from 20% to 10% • 50% reduction in error • 99.90% to 99.99% = 90% reduction in error • 50% to 75% = 50% reduction in error, can be applied to many other measures

119. Deriving Knowledge from Data at Scale Performance Metrics Precision and Recall • Typically used in document retrieval • Precision: – how many of the returned documents are correct – precision (threshold) • Recall: – how many of the positives does the model return – recall (threshold)

120. Deriving Knowledge from Data at Scale Performance Metrics Precision and Recall

121. Deriving Knowledge from Data at Scale

122. Deriving Knowledge from Data at Scale Next week we will go deeper with ROC curves, kappa, lift charts, etc…

123. Deriving Knowledge from Data at Scale

124. Deriving Knowledge from Data at Scale • Opening Discussion 30 minutes Review Discussion… • Ensembles, Random Forests 60 minutes • Break 5 minutes • Data Science Modelling 30 minutes Model performance evaluation… • Machine Learning Boot Camp ~60 minutes Clustering, k-Means… • Close

125. Deriving Knowledge from Data at Scale similar unsupervised learning data exploration

126. Deriving Knowledge from Data at Scale grouping within a group are similar and different from (or unrelated to) the objects in other groups Inter-cluster distances are maximized Intra-cluster distances are minimized

127. Deriving Knowledge from Data at Scale • Outliers objects that do not belong to any cluster outlier analysis cluster outliers

128. Deriving Knowledge from Data at Scale data reduction natural clusters useful outlier detection

129. Deriving Knowledge from Data at Scale How many clusters? Four ClustersTwo Clusters Six Clusters

130. Deriving Knowledge from Data at Scale hierarchical partitional

131. Deriving Knowledge from Data at Scale Original Points A Partitional Clustering

132. Deriving Knowledge from Data at Scale p4 p1 p3 p2 p4 p1 p3 p2 p4p1 p2 p3 p4p1 p2 p3 Traditional Hierarchical Clustering Non-traditional Hierarchical Clustering Non-traditional Dendrogram Traditional Dendrogram

133. Deriving Knowledge from Data at Scale

134. Deriving Knowledge from Data at Scale Single Linkage: Minimum distance* * Complete Linkage: Maximum distance* * Average Linkage: Average distance* * * * Wards method: Minimization of within-cluster variance * * * * * ¤ * * * * ¤ Centroid method: Distance between centres * * * * * ** * * * ¤ ¤ Non overlapping Overlapping Hierarchical Non-hierarchical 1a 1b 1c 1a 1b 1b1 1b22 Agglomerative Divisive

135. Deriving Knowledge from Data at Scale d(x, y) x y metric • d(i, j)  0 non-negativity • d(i, i) = 0 isolation • d(i, j) = d(j, i) symmetry • d(i, j) ≤ d(i, h)+d(h, j) triangular inequality real, boolean, categorical, ordinal

136. Deriving Knowledge from Data at Scale p = 2, L2 Euclidean distance weighted distance )||...| 22 || 11 (|),( 222 d y d xyxyxyxd  )||...| 22 | 2 | 11 | 1 (),( 222 d y d x d wxxwxxwyxd  d y d x d wyxwyxwyxd  ... 222111 ),(

137. Deriving Knowledge from Data at Scale Q1 Q2 Q3 Q4 Q5 Q6 X 1 0 0 1 1 1 Y 0 1 1 0 1 0 • Jaccard similarity between binary vectors X and Y • Jaccard distance between binary vectors X and Y Jdist(X,Y) = 1- JSim(X,Y) • Example: • JSim = 1/6 • Jdist = 5/6 YX YX YXJSim   ),(

138. Deriving Knowledge from Data at Scale • Lp Minkowski p p = 1, L1 Manhattan (or city block) p d i i y i x pp d x d x p yx p yxyxpL /1 1 )( /1 ||...| 22 || 11 |),(                         d i i y i x d y d xyxyxyxL 1 ||...| 22 |||),( 1 11

139. Deriving Knowledge from Data at Scale centroid

140. Deriving Knowledge from Data at Scale

141. Deriving Knowledge from Data at Scale -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 0 0.5 1 1.5 2 2.5 3 x y -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 0 0.5 1 1.5 2 2.5 3 x y Sub-optimal Clustering -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 0 0.5 1 1.5 2 2.5 3 x y Optimal Clustering Original Points

142. Deriving Knowledge from Data at Scale -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 0 0.5 1 1.5 2 2.5 3 x y Iteration 1 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 0 0.5 1 1.5 2 2.5 3 x y Iteration 2 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 0 0.5 1 1.5 2 2.5 3 x y Iteration 3 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 0 0.5 1 1.5 2 2.5 3 x y Iteration 4 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 0 0.5 1 1.5 2 2.5 3 x y Iteration 5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 0 0.5 1 1.5 2 2.5 3 x y Iteration 6

143. Deriving Knowledge from Data at Scale -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 0 0.5 1 1.5 2 2.5 3 x y Iteration 1 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 0 0.5 1 1.5 2 2.5 3 x y Iteration 2 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 0 0.5 1 1.5 2 2.5 3 x y Iteration 3 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 0 0.5 1 1.5 2 2.5 3 x y Iteration 4 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 0 0.5 1 1.5 2 2.5 3 x y Iteration 5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 0 0.5 1 1.5 2 2.5 3 x y Iteration 6

144. Deriving Knowledge from Data at Scale    K i Cx i i xmdistSSE 1 2 ),(

145. Deriving Knowledge from Data at Scale -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 0 0.5 1 1.5 2 2.5 3 x y Iteration 1 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 0 0.5 1 1.5 2 2.5 3 x y Iteration 2 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 0 0.5 1 1.5 2 2.5 3 x y Iteration 3 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 0 0.5 1 1.5 2 2.5 3 x y Iteration 4 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 0 0.5 1 1.5 2 2.5 3 x y Iteration 5

146. Deriving Knowledge from Data at Scale -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 0 0.5 1 1.5 2 2.5 3 x y Iteration 1 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 0 0.5 1 1.5 2 2.5 3 x y Iteration 2 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 0 0.5 1 1.5 2 2.5 3 x y Iteration 3 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 0 0.5 1 1.5 2 2.5 3 x y Iteration 4 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 0 0.5 1 1.5 2 2.5 3 x y Iteration 5

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148. Deriving Knowledge from Data at Scale

149. Deriving Knowledge from Data at Scale • Boolean Values • Categories

150. Deriving Knowledge from Data at Scale

151. Deriving Knowledge from Data at Scale

152. Deriving Knowledge from Data at Scale That’s all for tonight….

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