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Machine Learning 1 Machine(Learning(in(a(Nutshell ( Data$ Model$ - PowerPoint PPT Presentation

Jul 11, 2023 •537 likes •718 views

Machine Learning 1 Machine(Learning(in(a(Nutshell ( Data$ Model$ Performance$ Measure$ Machine(Learner( 2 Machine(Learning(in(a(Nutshell ( Data$ Model$ Performance$ Measure$ Machine(Learner( Data$with$a5ributes$ ID( A1( Reflex(

  1. Machine Learning 1

  2. Machine(Learning(in(a(Nutshell ( Data$ Model$ Performance$ Measure$ Machine(Learner( 2

  3. Machine(Learning(in(a(Nutshell ( Data$ Model$ Performance$ Measure$ Machine(Learner( Data$with$a5ributes$ ID( A1( Reflex( RefLow(RefHigh(Label( 1$ 5.6$ Normal$ 3.4$ 7$ No$ 2$ 5.5$ Normal$ 2.4$ 5.7$ No$ 3$ 5.3$ Normal$ 2.4$ 5.7$ Yes$ 4$ 5.3$ Elevated$ 2.4$ 5.7$ No$ 5$ 6.3$ Normal$ 3.4$ 7$ No$ 6$ 3.3$ Normal$ 2.4$ 5.7$ Yes$ 7$ 5.1$ Decreased$ 2.4$ 5.7$ Yes$ 8$ 4.2$ Normal$ 2.4$ 5.7$ Yes$ …$…$ …$ …$ …$ …$ Instance$$$ x i ∈ X with$label$ y i ∈ Y 3

  4. Machine(Learning(in(a(Nutshell ( Data$ Model$ Performance$ Measure$ Machine(Learner( Data$with$a5ributes$ Model$ f : X 7! Y LogisHc$regression$ ID( A1( Reflex( RefLow(RefHigh(Label( 1$ 5.6$ Normal$ 3.4$ 7$ No$ Support$vector$$ 2$ 5.5$ Normal$ 2.4$ 5.7$ No$ machines$ 3$ 5.3$ Normal$ 2.4$ 5.7$ Yes$ 4$ 5.3$ Elevated$ 2.4$ 5.7$ No$ 5$ 6.3$ Normal$ 3.4$ 7$ No$ 6$ 3.3$ Normal$ 2.4$ 5.7$ Yes$ Mixture$ 7$ 5.1$ Decreased$ 2.4$ 5.7$ Yes$ Models$ 8$ 4.2$ Normal$ 2.4$ 5.7$ Yes$ …$…$ …$ …$ …$ …$ x 1 Hierarchical$ Bayesian$ Instance$$$ x 2 x 3 x i ∈ X Networks$ with$label$ y i ∈ Y x 4 x 5 4

  5. Machine(Learning(in(a(Nutshell ( Data$ Model$ Performance$ Measure$ Machine(Learner( Data$with$a5ributes$ Model$ EvaluaHon$ f : X 7! Y Measure$predicted$labels$vs$ LogisHc$regression$ ID( A1( Reflex( RefLow(RefHigh(Label( actual$labels$on$test$data$ 1$ 5.6$ Normal$ 3.4$ 7$ No$ Support$vector$$ 2$ 5.5$ Normal$ 2.4$ 5.7$ No$ machines$ 3$ 5.3$ Normal$ 2.4$ 5.7$ Yes$ Learning$Curve$ 4$ 5.3$ Elevated$ 2.4$ 5.7$ No$ 5$ 6.3$ Normal$ 3.4$ 7$ No$ 6$ 3.3$ Normal$ 2.4$ 5.7$ Yes$ Mixture$ Performance$ 7$ 5.1$ Decreased$ 2.4$ 5.7$ Yes$ Models$ 8$ 4.2$ Normal$ 2.4$ 5.7$ Yes$ …$…$ …$ …$ …$ …$ x 1 Hierarchical$ Bayesian$ Instance$$$ x 2 x 3 x i ∈ X Networks$ with$label$ #$Training$Examples$ y i ∈ Y x 4 x 5 5

  6. A training set 6

  7. ID3-induced decision tree 7

  8. Model spaces I I - - - - + + + + I Nearest - neighbor Decision - tree + + Version space 8

  9. Decision tree-induced partition – example I Color green blue red Size + Shape big small square round - + Size + big small - + 9

  10. The Naïve Bayes Classifier Some material adapted from slides by Tom Mitchell, CMU. 19

  11. The Naïve Bayes Classifier ! Recall Bayes rule: P ( Y ) P ( X | Y ) i j i P ( Y | X ) = i j P ( X ) j ! Which is short for: P ( Y y ) P ( X x | Y y ) = = = i j i P ( Y y | X x ) = = = i j P ( X x ) = j ! We can re-write this as: P ( Y y ) P ( X x | Y y ) = = = i j i P ( Y y | X x ) = = = i j P ( X x | Y y ) P ( Y y ) ∑ = = = j k k k 20

  12. Deriving Naïve Bayes ! Idea: use the training data to directly estimate: P ( Y ) P ( X | Y ) and ! Then, we can use these values to estimate using Bayes rule. P ( Y | X ) new ! Recall that representing the full joint probability P ( X , X , … , X | Y ) is not practical. 1 2 n 21

  13. Deriving Naïve Bayes ! However, if we make the assumption that the attributes are independent, estimation is easy! P ( X 1 … , , X | Y ) P ( X | Y ) ∏ = n i i ! In other words, we assume all attributes are conditionally independent given Y. ! Often this assumption is violated in practice, but more on that later… 22

  14. Deriving Naïve Bayes X X 1 … , , X = ! Let and label Y be discrete. n P ( i Y ) P ( X i Y | ) ! Then, we can estimate and i directly from the training data by counting! Sky Temp Humid Wind Water Forecast Play? sunny warm normal strong warm same yes sunny warm high strong warm same yes rainy cold high strong warm change no sunny warm high strong cool change yes P(Sky = sunny | Play = yes) = ? P(Humid = high | Play = yes) = ? 23

  15. The Naïve Bayes Classifier ! Now we have: P ( Y y ) P ( X | Y y ) ∏ = = j i j P ( Y y | X 1 … , , X ) i = = j n P ( Y y ) P ( X | Y y ) ∑ ∏ = = k i k k i which is just a one-level Bayesian Network P ( Y ) Y Labels (hypotheses) P ( H ) j j i P ( X i Y | ) j … … Attributes (evidence) X X X 1 i n ! To classify a new point X new : Y arg max P ( Y y ) P ( X | Y y ) ∏ ← "" = = new k i k y k i 24

  16. The Naïve Bayes Algorithm ! For each value y k ! Estimate P(Y = y k ) from the data. ! For each value x ij of each attribute X i ! Estimate P(X i =x ij | Y = y k ) ! Classify a new point via: Y arg max P ( Y y ) P ( X | Y y ) ∏ ← "" = = new k i k y k i ! In practice, the independence assumption doesn � t often hold true, but Naïve Bayes performs very well despite it. 25

  17. Naïve Bayes Applications ! Text classification ! Which e-mails are spam? ! Which e-mails are meeting notices? ! Which author wrote a document? ! Classifying mental states Learning P(BrainActivity | WordCategory) Pairwise Classification Accuracy: 85% 26 People Words Animal Words

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