DEMYSTIFYINGMACHINE LEARNING

Alejandro Correa Bahnsen

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BLACK BOXMODELS

Machine learning modelsare often dismissed on thegrounds of lack ofinterpretability.When using advancedmodels it is nearlyimpossible to understandhow a model is making aprediction.

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MNIST - ACCU VS # PARAMS

Notebook to create the plot3

LIME

stands for LocalInterpretable Model-agnostic Explanations, andits objective is to explainthe result from anyclassifier so that a humancan understand individualpredictions

LIME

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LIMEAn interpretable representation is a point in a spacewhose dimensions can be interpreted by a human.LIME frames the search for an interpretableexplanation as an optimization problem. Given a setG of potentially interpretable models, we need ameasure L(f,g,x) of how poorly the interpretablemodel g∈∈G approximates the original model f forpoint x this is the loss function. We also need somemeasure Ω(g) of the complexity of the model (e.g. thedepth of a decision tree). We then pick a model whichminimizes both of these

ξ(x) = argmin g∈∈G L(f,g,x)+Ω(g)

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LIME

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LIME EXAMPLEURL PHISHING DETECTION

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URL PHISHING CLASSIFIERObjective: Evaluate phishingprobability using only theweb site URL

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URL PHISHING CLASSIFIER

Train a random forest

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LIME EXAMPLE

Fit lime explainer

Explain an instance

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LIME EXAMPLEExample Phishing URL

Phishing probability1.0

Url = http://login.paypal.com.convexcentral.com/Update/ab770f624342b07b71e56c1bae5d9bcb/

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LIME EXAMPLEExample Phishing URL

Phishing probability0.0283

Url = ...

http://www.redeyechicago.com/entertainment/tv/redeye-banshee-ivana-mili

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THANK YOU

FULL NOTEBOOK INHTTPS://GITHUB.COM/ALBAHNSEN/TALK

_DEMYSTIFYING_MACHINE_LEARNING

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