Questions tagged [k-fold]

A technique in cross-validation where the data is partitioned into k subsets (or "folds"), where the first k-1 folds are used for training and the last fold for evaluation. The process is repeated k times, leaving out a different fold for evaluation each time.

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K-fold cross validation to reduce overfitting : problem with the implementation

It is the first time I am trying to use cross-validation and I am facing an error. Firstly my dataset looks like this : So, in order to avoid/reduce the overfitting of my model I am trying to use a k-fold cross validation. from…
user15565396
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Does K-Fold iteratively train a model

If you run cross-val_score() or cross_validate() on a dataset, is the estimator trained using all the folds at the end of the run? I read somewhere that cross-val_score takes a copy of the estimator. Whereas I thought this was how you train a model…
Bryon
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Applying the KFold Cross Validation on nested dictionary

Input dictionary new_dict1 = {'ABW':{'ABR':1,'BPR':1,'CBR':1,'DBR':0},'BCW':{'ABR':0,'BPR':0,'CBR':1,'DBR':0}, 'CBW':{'ABR':1,'BPR':1,'CBR':0,'DBR':0},'MCW':{'ABR':1,'BPR':1,'CBR':0,'DBR':1}, …
Noorulain Islam
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model.fit in a for loop, for K-fold cross validation

I am trying to code a K-fold cross validation with LSTM architecture. But I got an this error (edit): Traceback (most recent call last): File "/Users/me/Desktop/dynamicsoundtreatments/DST-features-RNN.py", line 58, in
nbrc
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Error in singlefold(obs, k) : insufficient records:2, with k=5 / Unused argument (kfolds = 4)

The code I used: competition <- ENMevaluate(occ = occ_coord, env = env, bg.coords = bg_coord, method = "randomkfold", RMvalues=seq(0.5, 5, 0.5), fc = c("L", "LQ", "H", "LQH"), algorithm='maxent.jar') Unable to solve this, previously used a…
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object "i' not found in r forloop. k-fold cross validation

Like the tittle says, I am trying to do k-fold cross validation. my coding skills are very basic, please explain as simply as possible. """ library(ISLR) install.packages("ISLR") library(ISLR) install.packages("boot") library(boot) data <-…
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How to split a dataset into an overlapping or non-disjoint train-test using k-fold validation python

I am working on a quite different problem where I need to split a dataset into an overlapping or non-disjoint dataset using KFOLD validation in python. I was wondering if there is any way to do that.
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"Wrong model type for classification" while using Caret library in R (model with qualitative variable)

I am trying to use k-fold validation to find the better k for kNN. But while I run the following code, it appeared error of "Wrong model type for classification". I had referred to the previous similar question ("Wrong model type for classification"…
文子小
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Kfold CV in brms

I am trying to use kfold CV as a means of evaluating a model run using brms and I feel like I'm missing something. As a reproducible example, my data are structured as a binary response (0, 1) dependent on the length of an individual. Here is some…
user1997414
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cross_val_score writes that target variable is unknown

My target variable is Survived and has only 0 and 1 values, have my following code resulted in error: kfold = StratifiedKFold(n_splits=8,shuffle=True, random_state=42) rs = 15 clrs =…
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How to split train data and validation data properly in K fold cross validation

First, as a non-English speaker, I am using a translator to solve my problem. I ask for your understanding if the sentence is awkward and difficult to read. I try to learn data through Kfold cross validation. However, continuous errors occur in the…
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How can we include a prediction column in the initial dataset/dataframe after performing K-Fold cross validation?

I would like to run a K-fold cross validation on my data using a classifier. I want to include the prediction (or predicted probability) columns for each sample directly into the initial dataset/dataframe. Any ideas? from sklearn.metrics import…
Simon Provost
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Cannot read property 'overview' of undefined

I got the following error Cannot read property 'overview' of undefined I cannot figure out what this is.