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**Model Assessment and Selection**

Lecture Notes for Comp540 Chapter7 Jian Li Mar.2007

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Goal Model Selection Model Assessment

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**A Regression Problem y = f(x) + noise Can we learn f from this data?**

Let’s consider three methods...

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Linear Regression

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Quadratic Regression

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Joining the dots

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Which is best? Why not choose the method with the best fit to the data? “How well are you going to predict future data drawn from the same distribution?”

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**Model Selection and Assessment**

Model Selection: Estimating performances of different models to choose the best one (produces the minimum of the test error) Model Assessment: Having chosen a model, estimating the prediction error on new data

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**Why Errors Why do we want to study errors?**

In a data-rich situation split the data: Train Validation Test Model Selection Model assessment But, that’s not usually the case

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**Overall Motivation Errors Estimating the true error**

Measurement of errors (Loss functions) Decomposing Test Error into Bias & Variance Estimating the true error Estimating in-sample error (analytically ) AIC, BIC, MDL, SRM with VC Estimating extra-sample error (efficient sample reuse) Cross Validation & Bootstrapping

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**Measuring Errors: Loss Functions**

Typical regression loss functions Squared error: Absolute error:

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**Measuring Errors: Loss Functions**

Typical classification loss functions 0-1 Loss: Log-likelihood (cross-entropy loss / deviance):

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**The Goal: Low Test Error**

We want to minimize generalization error or test error: But all we really know is training error: ? And this is a bad estimate of test error

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**Bias, Variance & Complexity**

Training error can always be reduced when increasing model complexity, but risks over-fitting. Typically

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**Decomposing Test Error**

Model: For squared-error loss & additive noise: Deviation of the average estimate from the true function’s mean Irreducible error of target Y Expected squared deviation of our estimate around its mean

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**Further Bias Decomposition**

For linear models (eg. Ridge), bias can be further decomposed: * is the best fitting linear approximation For standard linear regression, Estimation Bias = 0 Average Model Bias Average Estimation Bias

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**Graphical representation of bias & variance**

Model Space (basic linear regression) Hypothesis Space Closest fit (given our observation) In population (if epsilon=0) Realization Shrunken fit Model Fitting Truth Regularized Model Space (ridge regression) Model Bias Estimation Variance Estimation Bias

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**Bias & Variance Decomposition Examples**

kNN Regression Linear Regression Averaging over the training set: Linear weights on y:

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**Simulated Example of Bias Variance Decomposition**

Prediction error = -- = -- Bias2 Regression with squared error loss Variance Bias-Variance different for 0-1 loss than for squared error loss <> -- <> -- Classification with 0-1 loss Estimation errors on the right side of the boundary don’t hurt!

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**Optimism of The Training Error Rate**

Typically: training error rate < true error (same data is being used to fit the method and assess its error) < overly optimistic

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**Adjustment for optimism of training error**

Estimating Test Error Can we estimate the discrepancy between err and Err? extra-sample error Expectation over N new responses at each xi Errin --- In-sample error: Adjustment for optimism of training error

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**Optimism Summary: for squared error, 0-1 and other loss functions:**

For linear fit with d indep inputs/basis funcs: optimism linearly with # d Optimism as training sample size

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**Ways to Estimate Prediction Error**

In-sample error estimates: AIC BIC MDL SRM Extra-sample error estimates: Cross-Validation Leave-one-out K-fold Bootstrap

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**Estimates of In-Sample Prediction Error**

General form of the in-sample estimate: For linear fit :

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**Similarly: Akaike Information Criterion (AIC)**

AIC & BIC Similarly: Akaike Information Criterion (AIC) Bayesian Information Criterion (BIC)

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AIC & BIC

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**MDL (Minimum Description Length)**

Regularity ~ Compressibility Learning ~ Finding regularities Learning model Input Samples Rn Predictions R1 Real class R1 Real model =? error

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**MDL (Minimum Description Length)**

Regularity ~ Compressibility Learning ~ Finding regularities Description of the model under optimal coding Length of transmitting the discrepancy given the model + optimal coding under the given model MDL principle: choose the model with the minimum description length Equivalent to maximizing the posterior:

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**SRM with VC (Vapnik-Chernovenkis) Dimension**

Vapnik showed that with probability 1- As h increases A method of selecting a class F from a family of nested classes

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Errin Estimation A trade-off between the fit to the data and the model complexity

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**Estimation of Extra-Sample Err**

Cross Validation Bootstrap

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Cross-Validation test train K-fold ……

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**Computation increases**

How many folds? Computation increases Variance decreases bias decreases k fold Leave-one-out k increases

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**Cross-Validation: Choosing K**

Popular choices for K: 5,10,N

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**Generalized Cross-Validation**

LOOCV can be computational expensive for linear fitting with large N Linear fitting For linear fitting under squared-error loss: GCV provides a computationally cheaper approximation

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**Bootstrap: Main Concept**

“The bootstrap is a computer-based method of statistical inference that can answer many real statistical questions without formulas” (An Introduction to the Bootstrap, Efron and Tibshirani, 1993) Step 2: Calculate the statistic Step 1: Draw samples with replacement

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**How is it coming Sampling distribution of sample mean**

In practice cannot afford large number of random samples The theory tells us the sampling distribution The sample stands for the population and the distribution of in many resamples stands for the sampling distribution

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**Bootstrap: Error Estimation with Errboot**

Depends on the unknown true distribution F A straightforward application of bootstrap to error prediction

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**Bootstrap: Error Estimation with Err(1)**

A CV-inspired improvement on Errboot

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**Bootstrap: Error Estimation with Err(.632)**

An improvement on Err(1) in light-fitting cases ?

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**Bootstrap: Error Estimation with Err(.632+)**

An improvement on Err(.632) by adaptively accounting for overfitting Depending on the amount of overfitting, the best error estimate is as little as Err(.632) , or as much as Err(1), or something in between Err(.632+) is like Err(.632) with adaptive weights, with Err(1) weighted at least .632 Err(.632+) adaptively mixes training error and leave-one-out error using the relative overfitting rate (R)

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**Bootstrap: Error Estimation with Err(.632+)**

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**Cross Validation & Bootstrap**

Why bother with cross-validation and bootstrap when analytical estimates are known? AIC, BIC, MDL, SRM all requires knowledge of d, which is difficult to attain in most situations. 2) Bootstrap and cross validation gives similar results to above but also applicable in more complex situation. 3) Estimating the noise variance requires a roughly working model, cross validation and bootstrap will work well even if the model is far from correct.

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**Conclusion Test error plays crucial roles in model selection**

AIC, BIC and SRMVC have the advantage that you only need the training error If VC-dimension is known, then SRM is a good method for model selection – requires much less computation than CV and bootstrap, but is wildly conservative Methods like CV, Bootstrap give tighter error bounds, but might have more variance Asymptotically AIC and Leave-one-out CV should be the same Asymptotically BIC and a carefully chosen k-fold should be the same BIC is what you want if you want the best structure instead of the best predictor Bootstrap has much wider applicability than just estimating prediction error

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