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4 votes
1 answer
50 views

Nested-cross validation pipeline and confidence intervals

I'm hoping someone can help me think through this. I've come across a lot of different resources on nested-cv, but I think I'm confused as to how to go about model selection and the appropriate ...
4 votes
3 answers
675 views

List of samples that each tree in a random forest is trained on in Scikit-Learn

In Scikit-learn's random forest, you can set bootstrap=True and each tree would select a subset of samples to train on. Is there a way to see which samples are used in each tree? I went through the ...
2 votes
1 answer
65 views

Question on bootstrap sampling

I have a corpus of manually annotated (aka "gold standard) documents and a collection of NLP systems annotations on the text from the corpus. I want to do a bootstrap sampling of the system and ...
0 votes
0 answers
9 views

Is this the correct way to model a sample statistic: Bayesian vs Bootstrapping

I have 2 samples S1 & S2. Contain lead level margins, converted 1 or 0 flag. Need to compare metric sum(margin) /sum(converted) for both. Sample size is low for frequentist. I tried bootstrapping ...
0 votes
0 answers
18 views

Is there a way to create a bootstrapped beta calibration function to use on new data?

I have created ML classification models that are now to be evaluated on a different population for external validation (n=5000, event rates between n=400 and n=1200 for different outcomes under study)....
0 votes
0 answers
33 views

Estimate confidence interval of a cubic B-spline fit

I have a non-linear system. I apply multiple inputs x(t) (t=time) and measure each response y(t). In other words, I have inputs x1(t), x2(t), x3(t), xn(t) and I measure y1(t), y2(t), y3(t), yn(t) for ...
1 vote
1 answer
16 views

Model evaluation approach allowing manual experimentation without data leakage

In supervised machine learning, are there any evaluation approaches beside using a fixed holdout test dataset, which allow me as a scientist to manually compare preprocessing approaches, without ...
3 votes
2 answers
302 views

How bootstrapping works for prediction intervals?

I'm experimenting with prediction interval (PI) over univariant time-data using skforecast pythonic package.. in the documentation it is mentioned that: Prediction intervals A prediction interval ...
0 votes
0 answers
92 views

Understanding the code for calculating the 95% confidence interval of AUC using bootstrapping

It's really embarrassing, but I lack statistical knowledge. I would like to find the confidence interval for AUC at 95%. Actually, I got the code from here(https://stackoverflow.com/questions/52373318/...
1 vote
0 answers
244 views

What is the best way to combine cross-validation and bootstrapping for one application?

We intend to model data with non-parametric covariate splines and we would like to understand the uncertainty of the parameter estimates/response estimates. Currently, we use cross-validation to model ...
0 votes
1 answer
212 views

Understanding bootstrapping in bias variance decomposition

I was going through bias and variance tradeoff article and it makes use of bias_variance_decomp function from mlxtend library. ...
1 vote
0 answers
83 views

How to perform bootstrap validation on CART decision tree?

I have a relatively small dataset n = 500 for which I am training a CART decision tree. My dataset has about 30 variables and the outcome has 3 classes. I am using CART for interpretability purposes, ...
1 vote
0 answers
64 views

Evaluate Dendrogram Statistical Significance

I have N=21 objects and each one has about 80 possible not NaN descriptors. I carried out a hierarchical clustering on the objects and I obtained this dendrogram. I want some kind of 'confidence' ...
0 votes
1 answer
185 views

Perform bootstrapping of an ordinary linear regression model, using B=100 bootstrap resamples of my dataset, and getting RMSE

So Im studying machine learning through R, and Im working with the boston data set from the library MASS. I am practicing bootsrapping. I already carried out analysis to determine how ,many distinct ...
1 vote
0 answers
35 views

Stratified sampling - use of proxy variable

For splitting of the data into train/test/val I use stratified sampling. Is it appropriate to define strata using information extracted from the dataset? E.g. use machine-learning to model proxy ...
1 vote
0 answers
15 views

How are the same observation sets treated in Random Forests with Bootstrapping?

Let's assume an extremely small dataset with only 4 observations. And I create a Random Forest model, with a quite large number of trees, say 200. If so, some sample sets that are the same each other ...
1 vote
1 answer
2k views

nnet in caret. Bootstrapping or cross-validation?

I want to train shallow neural network with one hidden layer using nnet in caret. In trainControl, I used method = "cv" to perform 3-fold cross-validation. The snipped the code and results ...
2 votes
2 answers
287 views

Resampling train and test data in R

I need to try out few different machine learning methods (SVM, Logistic regression etc.), predict a value either true or false, and write down their AUC and Accuracy of these predictions. I have ...
3 votes
0 answers
151 views

Difference Bagging and Bootstrap aggregating

Bootstrap belongs to Efron. Tibshirani wrote a book about that in reference to Efron. Bootstrap process for estimating the standard error of statistic s(x). B bootstrap sample are generatied from ...
1 vote
1 answer
121 views

About confidence/prediction intervals: parametric methods VS non-parametric (via bootstrap) methods

About the methodology to find confidence and/or prediction intervals in, let's say, a regression problem, I know 2 main options: Checking normality in the estimates/predictions distribution, and ...