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Results tagged with cross-validation
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user 17696
Refers to general procedures that attempt to determine the generalizability of a statistical result. Cross-validation arises frequently in the context of assessing how a particular model fit predicts future observations. Methods for cross-validation usually involve withholding a random subset of the data during model fitting and quantifying how accurate the withheld data are predicted and repeating this process to get a measure of prediction accuracy.
1
vote
Will cross validation performance be an accurate indication for predicting the true performa...
The theory behind cross validation ( v-fold cross validation) has been addressed in many papers. There is a proof for that in a set papers published from 2003-2007.
Please refer to :
- oracle selector …
- 2,019
10
votes
Accepted
Can overfitting occur even with validation loss still dropping?
I am not sure if the validation set is balanced or not. You have a severe data imbalance problem. If you sample equally and randomly from each class to train your network, and then a percentage of wha …
- 2,019
2
votes
Cross validation when training neural network?
Best by cross validation
The whole data can be divided into training and testing.
You can not touch the testing data set for any kind of training. Keep it away!!
For the training data you can use i …
- 2,019
0
votes
Model selection and assessment using leave-one-out cross validation
What you just said is dividing the data into three parts: training, validating and testing.
This is a very common practice in machine learning.
We use validation to help in selecting hyper parameters …
- 2,019
4
votes
Using Cross Validation technique for a CNN model
The previous answer already got accepted, but I am answering this question just to make sure that things are clear. I will go one step deeper which can be helpful to advanced people.
First of all, cr …
- 2,019
2
votes
Optimizing decision threshold on model with oversampled/imbalanced data
I am not sure if in the last point, you meant the validation set instead of the testing set.
Here is my advice:
1- understand the impact of having data imbalance. Let start with understanding the dif …
- 2,019
1
vote
Hyperparameter tuning for stacked models
Do not mix dividing the data into k-fold with cross validation.
You can use the 4 folds ( training data) to optimize the base classifiers. You can also find the best hyper parameters by applying cros …
- 2,019