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scikit-learn guidelines describe the typical motivation for nested cross-validation. They [say](https://scikit-learn.org/stable/auto_examples/model_selection/plot_nested_cross_validation_iris.html) …
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What is the recommended way to do K-fold cross validation with a webdataset? I wasn't able to find any examples on it.
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How can I fine tune the emotion2vec+large model on another dataset without using the process that you have used for iemocap?
I have tried to use four features and your bash script train.sh but I …
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Unless we want to do the kfold by hand, it might be a good idea to cut the data input (once it is loaded into the array) into 5 parts. That way we can do 5fold cross validation automatically, running …
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from machine_learning_algorithm.cross_validation import validate
ModuleNotFoundError: No module named 'machine_learning_algorithm'
请问一下,您这个模块是没有上传到github上吗
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- evaluation pipeline
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**What happened**:
I'm currently trying to create a pipeline for model training using `LogisticRegression` and Nested cross-validation. I've got an unexpected `AttributeError` during the pipeline exe…
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Checkout this revision: 85b6087b2917014530096c4f65c78c15189f966a
then run:
ds = prtDataGenUnimodal;
class = prtClassFld;
ds = class.kfolds(ds,10);
disp(ds)
You will see ds has userData i…
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I think rather than make a branch for different arbitrary choices of K, I should so I more formal train/test split and validation to optimize K.
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classification_model(model, df,predictors_Logistic,outcome_var)
test_modified.to_csv("Logistic_Prediction.csv",columns=['Loan_ID','Loan_Status'])
TypeError Traceb…