blinq media praneeth vepakomma senior data scientist
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Generalization in Supervised Machine Learning. BLiNQ MEDIA Praneeth Vepakomma Senior Data Scientist. Hypothetical Knapsack of Coins:. Copper and Gold Coins Total number of coins is fixed and is a large sample. Capture-Recapture What is the proportion of Gold coins?. - PowerPoint PPT PresentationTRANSCRIPT
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BLiNQ MEDIAPraneeth VepakommaSenior Data Scientist
Generalization in Supervised
Machine Learning
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Hypothetical Knapsack of Coins:
Copper and Gold CoinsTotal number of coins is fixed and is a large sample.Capture-RecaptureWhat is the proportion of Gold coins?
Copper and Gold CoinsTotal number of coins is variable and is a large sample.Capture-RecaptureWhat is the proportion of Gold coins?
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BASIC ML/STAT TERMINOLOGY:
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190 Years after Gauss, the core problem of prediction remains an active problem :
Then:
Now:
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190 Years after Gauss, the core problem of prediction remains an active problem :
Find a mapping♯ from the features:
#Approximation
is a list of parameters, required to represent the function
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ExistingFeatures
KnownLabels
UnavailableFeatures
UnknownLabels
Loss Function
Loss Function
Assumptions
What is Supervised Learning?
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Evaluating the Learned Function:
Loss Function quantifies the error in the approximation.
Learn a mapping by optimizing the loss.
Example:
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Predictions with varying parameters:
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Predictions with varying parameters:
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How do we generalize?
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Generalization and Predictability
Empirical Risk Minimization:
True Risk Minimization:
Empirical Risk is the average (expected) loss on seen data.
True Risk is the expected risk on the process generating the X,Y pairs.
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PARAMETRIC CHARACTERIZATION OF THE MAPPING :
2d-Linear function: Slope, InterceptCubic Spline: Number of knots, Location of KnotsNearest-Neighbor regression: Number of neighborsLasso: L1-L2 WeightsSupport Vector Machines: Kernel width, Margin LengthRandom Forests: Resampling sample size
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Long list of available Supervised Learning Techniques.
Most of the techniques have tuning parameters.
We can minimize out-of-sample performance by tuning the technique with optimal parameters.
Tuning can be performed by cross-validation over a discrete grid of parameter combinations.
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CURSE OF DIMENSIONALITY-Flat World-10D World:
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CURSE OF DIMENSIONALITY-Flat World-10D World:
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CURSE OF DIMENSIONALITY-Flat World-10D World:
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CURSE OF DIMENSIONALITY-Let us validate:
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Structural Risk Minimization via Regularization:
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Brief Description
Technology Overview
Hiring (What we’re looking for)http://blinqmedia.com/contact/job-openings/
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Lets work with Abalone
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Thank You!