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Y. Kawamata, “The minimal discrepancy coefficients of terminal singularities of dimension 3”, Appendix to [26],Izv. Akad. Nauk SSSR. Ser. Mat.,56, No.

**Table of contents**

- 3-FOLD LOG FLIPS
- ojyziwotem.tkaic geometry - Flips in the Minimal Model Program - MathOverflow
- How to use Cross-Validate Model
- 3-Fold log models

Since credit scoring is a classification problem, I will use the number of misclassified observations as the loss measure. The data set contains information about 4, individuals for the following variables:. The tidy data are contained in the file CleanCreditScoring.

**inspirarte-qa-fabercastell.gingaone.com/hairbutt-the-hippo-1.php**

## 3-FOLD LOG FLIPS

The caret package provides functions for splitting the data as well as functions that automatically do all the job for us, namely functions that create the resampled data sets, fit the models, and evaluate performance. Among the functions for data splitting I just mention createDataPartition and createFolds. In both functions the random sampling is done within the levels of y when y is categorical to balance the class distributions within the splits.

These functions return vectors of indexes that can then be used to subset the original sample into training and test sets. To automatically split the data, fit the models and assess the performance, one can use the train function in the caret package. The code below shows an example of the train function on the credit scoring data by modeling the outcome using all the predictors available with a penalized logistic regression.

More specifically, I use the glmnet package Friedman, Hastie, and Tibshirani , that fits a generalized linear model via penalized maximum likelihood. The train function requires the model formula together with the indication of the model to fit and the grid of tuning parameter values to use. Finally, the preProcess argument allows to apply a series of pre-processing operations on the predictors in our case, centering and scaling the predictor values.

Then, it is possible to predict new samples with the identified optimal model using the predict method:. Friedman, J. Hastie, and R. Hastie, T.

Tibshirani, and J. The Elements of Statistical Learning. James, G. Witten, T. An Introduction to Statistical Learning. Zou, H. Lecturer of Statistics at Bocconi University. R enthusiast. You must be logged in to post a comment. This site uses Akismet to reduce spam. Learn how your comment data is processed.

Introduction Since ancient times, humankind has always avidly sought a way to predict the future. The Bias-Variance Dilemma The reason why one should care about the choice of the tuning parameter values is because these are intimately linked with the accuracy of the predictions returned by the model. At this point, it is important to distinguish between different prediction error concepts: the training error , which is the average loss over the training sample , the test error , the prediction error over an independent test sample.

Generate the training and test samples.

## ojyziwotem.tkaic geometry - Flips in the Minimal Model Program - MathOverflow

Fit the models. Plot the data. Compute the training and test errors for each model. Compute the average training and test errors. Plot the errors. The first term is the data generating process variance. This term is unavoidable because we live in a noisy stochastic world, where even the best ideal model has non-zero error. The second term originates from the difficulty to catch the correct functional form of the relationship that links the dependent and independent variables sometimes it is also called the approximation bias.

The last term is due to the fact that we estimate our models using only a limited amount of data. Fortunately, this terms gets closer and closer to zero as long as we collect more and more training data. Typically, the more complex i. Clearly, the situation illustrated above is only ideal, because in practice: We do not know the true model that generates the data. Indeed, our models are typically more or less mis-specified. We do only have a limited amount of data. A Solution: Cross-Validation In essence, all these ideas bring us to the conclusion that it is not advisable to compare the predictive accuracy of a set of models using the same observations used for estimating the models.

Numeric mean median var sd valid. Mode good. Mode owner. Mode married. Mode fixed. Mode sen -1,1]. Mode time 48,99]. Mode age 30,40]. Mode exp 0,40]. Mode inc 80,]. Mode asset -1,0]. Mode debt -1,0]. Mode am ,1. Mode priz 1. Mode finr 80,90]. Mode sav 2,4]. List of Warning: package 'Matrix' was built under R version 3. Resampling results across tuning parameters: alpha lambda Accuracy Kappa 0. Pre-processing: centered 68 , scaled Resampling: Cross-Validated 10 fold. Summary of sample sizes: , , , , , , Resampling results across tuning parameters:.

References Efron, B. An Introduction to the Bootstrap. In some sense there might be other ways to pick a representative, but one might argue that a minimal model is the "simplest" model that is still smooth make a note of this, we will realize later that here smoothness is actually something else in disguise.

Let's contract everything we can. OK, so the strategy is to contract as much stuff as we can and hope that this way we get a reasonable theory. In fact there is a more precise way to say this, but let me not get into technical details now. The claim is that every variety is birational to one that is a series of Fano fiber spaces over a minimal variety.

At the time this was thought of as proof that minimal models did not exist in higher dimensions, but then Reid and Mori realized that it only means that minimal models need not be smooth. He says it is too ambitious, but it may not be absolutely clear to everyone that this means impossible--as stated. The thing is, minimal models have no worse than terminal singularities. So, one could argue that even minimal models of surfaces have terminal singularities, that is, that's the natural class of singularities for a minimal model.

In general this is how we might end up with a Fano fibre space. If the contraction is birational, then there are still two possibilities: it is a divisorial contraction or a small contraction. The former means that the exceptional set is a divisor, the latter that it is smaller than that. Now, already the former can bring in singularities, but they are not so bad and the program can continue.

When the contraction is small, there are several problems. Simply put the singularities become too bad. OK, you have to adjust this slightly for singularities, but I am not writing a precise paper here.

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So, the idea of the flip is this: let's change the normal bundle of the curve. So, let's "cut it out" and put it back with the opposite normal bundle, so in a "flipped" way. I guess I wrote a whole bunch of things just to say that and some people have said similar things already, but perhaps this little essay gives some new insight. To answer your question about whether a similar construction exists elsewhere, the answer is "yes".

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A "flip" is like a "surgery" in topology. But I am no expert on that. Actually, just to include a disclaimer: I am not claiming to be an expert on flips either. There are two kinds of contractions performed in the process of MMP, divisorial contractions and small contractions. While the divisorial contractions preserve the condition, Q-factoriality, the small contractions don't.

That means we can no longer check the nefness of K and we cannot resume MMP. Flips fix this problem. For a curve C in the extremal ray inducing a small contraction, a flip 'flips' the negative interesection K.

### How to use Cross-Validate Model

C to positive. It is a condimension one surgery on X, which preserves the Q-factoriality. So we can run MMP again without worrying about the 'bad' curve C. Corti's 'What is I've been told that the intuition behind flips are that when you get an extremely singular image as jvp mentioned is REALLY the problem, not singularities as a rule then it means you have a curve "in the wrong place" so you cut out a curve and glue it back in differently, roughly speaking.

That is, you "flip" the curve around, so that when you do a contraction, things workout more nicely. Caveat: I'm learning this material right now, so this may be bad intuition, but it's what I've been told. Sign up to join this community.

## 3-Fold log models

The best answers are voted up and rise to the top. Home Questions Tags Users Unanswered. Asked 9 years, 10 months ago. Active 2 years, 2 months ago. Viewed 3k times. If I understand correctly, in Mulmuley's work "flip" refers to a formal translation of a statement in logic so it has nothing to do with anything actually geometric. I suppose the fact that he uses algebraic geometry in complexity theory may be confusing, but I think his flips are not geometric.