Polynomial Curve Fitting BITS C464/BITS F464 Navneet Goyal Department of Computer Science,...

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Polynomial Curve FittingBITS C464/BITS F464

Navneet Goyal

Department of Computer Science, BITS-Pilani, Pilani Campus, India

Polynomial Curve Fitting Seems a very trivial concept!! Why are we discussing it in Machine Learning

course? A simple regression problem!! It motivates a number of key concepts of ML!! Let’s discover…

Polynomial Curve Fitting

Observe Real-valuedinput variable x• Use x to predict valueof target variable t• Synthetic datagenerated fromsin(2π x)• Random noise intarget values

Input Variable

Reference: Christopher M Bishop: Pattern Recognition & Machine Leaning, 2006 Springer

Input Variable

N observations of xx = (x1,..,xN)Tt = (t1,..,tN)T• Goal is to exploit trainingset to predict value offrom x• Inherently a difficultproblemData Generation:

N = 10Spaced uniformly in range [0,1]Generated from sin(2πx) by addingsmall Gaussian noiseNoise typical due to unobserved variables

Input Variable

• Where M is the order of the polynomial• Is higher value of M better? We’ll see shortly!• Coefficients w0 ,…wM are denoted by vector w• Nonlinear function of x, linear function of coefficients w• Called Linear Models

Sum-of-Squares Error Function

Polynomial curve fitting

Polynomial curve fitting Choice of M?? Called the model selection or model

comparison

0th Order Polynomial

Poor representations of sin(2πx)

1st Order Polynomial

Poor representations of sin(2πx)

3rd Order Polynomial

Best Fit to sin(2πx)

9th Order Polynomial

Over Fit: Poor representation of sin(2πx)

Polynomial Curve Fitting Good generalization is the objective Dependence of generalization performance on M? Consider a data set of 100 points Calculate E(w*) for both training data & test data Choose M which minimizes E(w*) Root Mean Square Error (RMS)

Sometimes convenient to use as division by N allows us to compare different sizes of data sets on equal footing

Square root ensures ERMS is measure on the same scale ( and in same units) as the target variable t

Flexibility & Model Complexity M=0, very rigid!! Only 1 parameter to play

Flexibility & Model Complexity M=1, not so rigid!! 2 parameters to play with!

Flexibility & Model Complexity So what value of M is most suitable?

Any Answers???

Over-fittingFor small M(0,1,2) Inflexible to handle oscillations of sin(2πx) M(3-8) flexible enough to handle oscillations of sin(2πx) For M=9 Too flexible!! TE = 0GE = high

Why is it happening?

Polynomial Coefficients

Data Set Size M=9- Larger the data set, the more complex model we can afford to fit to the data- No. of data pts should be no less than 5-10 times the no. of adaptive parameters in the model

Over-fitting Problem

Should we limit the no. of parameters according to the available training set?

Complexity of the model should depend only on the complexity of the problem!

LSE represents a specific case of Maximum Likelihood

Over-fitting is a general property of maximum likelihood

Over-fitting Problem can be avoided using the Bayesian Approach!

Over-fitting Problem

In Bayesian Approach, the effective number of parameters adapts automatically to the size of the data set

In Bayesian Approach, models can have more parameters than the number of data points

Regularization

Penalize large coefficient values

Regularization:

Regularization: vs.

Polynomial Coefficients

Take Away from Polynomial Curve Fitting Concept of over-fitting Model Complexity & Flexibility

Will keep revisiting it from time to time…

Polynomial Curve Fitting BITS C464/BITS F464 Navneet Goyal Department of Computer Science,...

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