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Evaluating Hypotheses • Sample error, true error • Confidence intervals for observed hypothesis error • Estimators • Binomial distribution, Normal distribution, Central Limit Theorem • Paired t-tests • Comparing Learning Methods CS 8751 ML & KDD Evaluating Hypotheses 1 Problems Estimating Error 1. Bias: If S is training set, errorS(h) is optimistically biased bias E[errorS (h)] errorD (h) For unbiased estimate, h and S must be chosen independently 2. Variance: Even with unbiased S, errorS(h) may still vary from errorD(h) CS 8751 ML & KDD Evaluating Hypotheses 2 Two Definitions of Error The true error of hypothesis h with respect to target function f and distribution D is the probability that h will misclassify an instance drawn at random according to D. errorD (h) Pr f ( x) h( x) xD The sample error of h with respect to target function f and data sample S is the proportion of examples h misclassifies 1 errorS (h) f ( x) h( x) n xS where f ( x) h( x) is 1 if f ( x) h( x), and 0 otherwise How well does errorS(h) estimate errorD(h)? CS 8751 ML & KDD Evaluating Hypotheses 3 Example Hypothesis h misclassifies 12 of 40 examples in S. 12 errorS (h) .30 40 What is errorD(h)? CS 8751 ML & KDD Evaluating Hypotheses 4 Estimators Experiment: 1. Choose sample S of size n according to distribution D 2. Measure errorS(h) errorS(h) is a random variable (i.e., result of an experiment) errorS(h) is an unbiased estimator for errorD(h) Given observed errorS(h) what can we conclude about errorD(h)? CS 8751 ML & KDD Evaluating Hypotheses 5 Confidence Intervals If • S contains n examples, drawn independently of h and each other • n 30 Then • With approximately N% probability, errorD(h) lies in interval errorS (h)(1 errorS (h)) errorS (h) z N n where N% : 50% 68% 80% 90% 95% 98% 99% z N : 0.67 1.00 1.28 1.64 1.96 2.33 2.53 CS 8751 ML & KDD Evaluating Hypotheses 6 Confidence Intervals If • S contains n examples, drawn independently of h and each other • n 30 Then • With approximately 95% probability, errorD(h) lies in interval errorS (h)(1 errorS (h)) errorS (h) 1.96 n CS 8751 ML & KDD Evaluating Hypotheses 7 errorS(h) is a Random Variable • Rerun experiment with different randomly drawn S (size n) • Probability of observing r misclassified examples: Binomial distribution for n=40, p=0.3 0.14 0.12 P(r) 0.10 0.08 0.06 0.04 0.02 0.00 0 P(r ) CS 8751 ML & KDD 5 10 15 20 r 25 30 35 40 n! errorD (h) r (1 errorD (h)) n r r!(n r )! Evaluating Hypotheses 8 Binomial Probability Distribution Binomial distribution for n=40, p=0.3 0.14 0.12 P(r ) P(r) 0.10 0.08 0.06 n! p r (1 p) n r r!(n r )! 0.04 0.02 0.00 0 5 10 15 20 r 25 30 35 40 Probabilty P(r) of r heads in n coin flips, if p Pr (heads) n Expected, or mean value of X : E[X] iP (i ) np i 0 Variance of X : Var(X) E[( X E[ X ]) 2 ] np(1 p ) Standard deviation of X : σ X E[( X E[ X ]) 2 ] np(1 p ) CS 8751 ML & KDD Evaluating Hypotheses 9 Normal Probability Distribution Normal distribution with mean 0, standard deviation 1 0.4 0.35 P(r ) 0.3 0.25 0.2 0.15 1 2πσ 12 xσμ 2 2 e 0.1 0.05 0 -3 -2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 3 The probabilit y that X will fall into the interval (a,b) is given by b a p( x)dx Expected, or mean value of X : E[X] μ Variance of X : Var(X) σ 2 Standard deviation of X : σ X σ CS 8751 ML & KDD Evaluating Hypotheses 10 Normal Distribution Approximates Binomial errors (h) follows a Binomial distributi on, with mean μ errorS ( h ) errorD (h) standard deviation σ errorS ( h ) errorD (h)(1 errorD (h)) n Approximat e this by a Normal distributi on with mean μ errorS ( h ) errorD (h) standard deviation σ errorS ( h ) CS 8751 ML & KDD errorS (h)(1 errorS (h)) n Evaluating Hypotheses 11 Normal Probability Distribution 0.4 0.35 0.3 0.25 0.2 0.15 0.1 0.05 0 -3 -2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 3 80% of area (probabili ty) lies in 1.28σ N% of area (probabili ty) lies in z Nσ N% : 50% 68% 80% 90% 95% 98% 99% zN : 0.67 1.00 1.28 1.64 1.96 2.33 2.53 CS 8751 ML & KDD Evaluating Hypotheses 12 Confidence Intervals, More Correctly If • S contains n examples, drawn independently of h and each other • n 30 Then • With approximately 95% probability, errorS(h) lies in interval errorD (h)(1 errorD (h)) errorD (h) 1.96 n • equivalently, errorD(h) lies in interval errorD (h)(1 errorD (h)) errorS (h) 1.96 n • which is approximately errorS (h)(1 errorS (h)) errorS (h) 1.96 n CS 8751 ML & KDD Evaluating Hypotheses 13 Calculating Confidence Intervals 1. Pick parameter p to estimate • errorD(h) 2. Choose an estimator • errorS(h) 3. Determine probability distribution that governs estimator • errorS(h) governed by Binomial distribution, approximated by Normal when n 30 4. Find interval (L,U) such that N% of probability mass falls in the interval • Use table of zN values CS 8751 ML & KDD Evaluating Hypotheses 14 Central Limit Theorem Consider a set of independen t, identicall y distribute d random variables Y1 Yn , all governed by an arbitrary probabilit y distributi on with mean and finite variance 2 . Define the sample mean 1 n Y Yi n i 1 Central Limit Theorem. As n , the distributi on governing Y σ2 approaches a Normal distributi on, with mean and variance . n CS 8751 ML & KDD Evaluating Hypotheses 15 Difference Between Hypotheses Test h1 on sample S1 , test h2 on S 2 1. Pick parameter to estimate d errorD (h1 ) errorD (h2 ) 2. Choose an estimator d errorS1 (h1 ) errorS 2 (h2 ) 3. Determine probabilit y distributi on that governs estimator σd errorS1 (h1 )(1 errorS1 (h1 )) n1 errorS 2 (h2 )(1 errorS 2 (h2 )) n2 4. Find interval (L, U) such that N% of probabilit y mass falls in the interval errorS1 (h1 )(1 errorS1 (h1 )) errorS 2 (h2 )(1 errorS 2 (h2 )) ˆ d zN n1 n2 CS 8751 ML & KDD Evaluating Hypotheses 16 Paired t test to Compare hA,hB 1. Partition data into k disjoint t est sets T1,T2 ,...,Tk of equal size, where this size is at least 30. 2. For i from 1 to k do δ i errorTi (hA ) errorTi (hB ) 3. Return the value d, where 1 k δ δ i k i 1 N% confidence interval estimate for d : δ t N,k-1sδ k 1 2 sδ ( δ δ ) i k (k 1) i 1 Note δ i approximately Normally distri buted CS 8751 ML & KDD Evaluating Hypotheses 17 N-Fold Cross Validation • Popular testing methodology • Divide data into N even-sized random folds • For n = 1 to N – – – – Train set = all folds except n Test set = fold n Create learner with train set Count number of errors on test set • Accumulate number of errors across N test sets and divide by N (result is error rate) • For comparing algorithms, use the same set of folds to create learners (results are paired) CS 8751 ML & KDD Evaluating Hypotheses 18 N-Fold Cross Validation • Advantages/disadvantages – Estimate of error within a single data set – Every point used once as a test point – At the extreme (when N = size of data set), called leave-one-out testing – Results affected by random choices of folds (sometimes answered by choosing multiple random folds – Dietterich in a paper expressed significant reservations) CS 8751 ML & KDD Evaluating Hypotheses 19 Results Analysis: Confusion Matrix • For many problems (especially multiclass problems), often useful to examine the sources of error • Confusion matrix: Expected ClassA CS 8751 ML & KDD Predicted ClassA ClassB ClassC 25 5 20 Total 50 ClassB 0 45 5 50 ClassC 25 0 25 50 Total 50 50 50 150 Evaluating Hypotheses 20 Results Analysis: Confusion Matrix • Building a confusion matrix – Zero all entries – For each data point add one in row corresponding to actual class of problem under column corresponding to predicted class • Perfect prediction has all values down the diagonal • Off diagonal entries can often tell us about what is being mis-predicted CS 8751 ML & KDD Evaluating Hypotheses 21 Receiver Operator Characteristic (ROC) Curves Originally from signal detection • • Becoming very popular for ML • Used in: – Two class problems – Where predictions are ordered in some way (e.g., neural network activation is often taken as an indication of how strong or weak a prediction is) • Plotting an ROC curve: – Sort predictions (right) by their predicted strength – Start at the bottom left – For each positive example, go up 1/P units where P is the number of positive examples – For each negative example, go right 1/N units where N is the number of negative examples CS 8751 ML & KDD Evaluating Hypotheses 22 ROC Curve True Positives (%) 100 75 50 25 0 25 50 75 100 False Positives (%) CS 8751 ML & KDD Evaluating Hypotheses 23 ROC Properties • Can visualize the tradeoff between coverage and accuracy (as we lower the threshold for prediction how many more true positives will we get in exchange for more false positives) • Gives a better feel when comparing algorithms – Algorithms may do well in different portions of the curve • A perfect curve would start in the bottom left, go to the top left, then over to the top right – A random prediction curve would be a line from the bottom left to the top right • When comparing curves: – Can look to see if one curve dominates the other (is always better) – Can compare the area under the curve (very popular – some people even do t-tests on these numbers) CS 8751 ML & KDD Evaluating Hypotheses 24