Table of Contents
- 1 What type of data is used for cross-validation for model training?
- 2 What is the gold standard validation strategy in ML?
- 3 What statistics does cross validation reduce?
- 4 What is generalized cross validation?
- 5 What is gold standard method?
- 6 Is cross validation necessary for large data sets?
- 7 What are the different types of cross validation?
- 8 What is cross validation and why do you need it?
- 9 How many percentage splits can you do in cross validation?
- 10 What is the difference between training set and validation set?
What type of data is used for cross-validation for model training?
In k-fold cross-validation, the original sample is randomly partitioned into k equal sized subsamples. Of the k subsamples, a single subsample is retained as the validation data for testing the model, and the remaining k − 1 subsamples are used as training data.
What is the gold standard validation strategy in ML?
Generally k-fold cross validation is the gold-standard for evaluating the performance of a machine learning algorithm on unseen data with k set to 3, 5, or 10. Using a train/test split is good for speed when using a slow algorithm and produces performance estimates with lower bias when using large datasets.
Is cross-validation always needed?
In general cross validation is always needed when you need to determine the optimal parameters of the model, for logistic regression this would be the C parameter.
What statistics does cross validation reduce?
This significantly reduces bias as we are using most of the data for fitting, and also significantly reduces variance as most of the data is also being used in validation set. Interchanging the training and test sets also adds to the effectiveness of this method.
What is generalized cross validation?
Generalized cross validation (GCV) is one of the most important approaches used to estimate parameters in the context of inverse problems and regularization techniques. A notable example is the determination of the smoothness parameter in splines.
What is a gold standard dataset?
In the case of a dataset, a gold standard would be one accepted as the most accurate and reliable of its kind, which could be used as a measure of those qualities in other datasets.
What is gold standard method?
A gold standard study may refer to an experimental model that has been thoroughly tested and has a reputation in the field as a reliable method. The correct interpretation of a diagnostic test demands one to master specific concepts such as sensitivity, specificity, prevalence, positive and negative predictive values.
Is cross validation necessary for large data sets?
First of all you should have a large dataset (which might introduce bias in the study) and at the same time a sufficient number of events (in order to create the k-fold). Both conditions are essential for the use of cross validation. The data set is separated into two sets, called the training set and the testing set.
What is cross validation in data science?
Cross validation is a technique for assessing how the statistical analysis generalises to an independent data set.It is a technique for evaluating machine learning models by training several models on subsets of the available input data and evaluating them on the complementary subset of the data.
What are the different types of cross validation?
Understanding 8 types of Cross-Validation
- Leave p out cross-validation.
- Leave one out cross-validation.
- Holdout cross-validation.
- Repeated random subsampling validation.
- k-fold cross-validation.
- Stratified k-fold cross-validation.
- Time Series cross-validation.
- Nested cross-validation.
What is cross validation and why do you need it?
It allows you to check your model performance on one dataset, which you use for training and testing. If you use a cross validation then you are, in fact, identifying the ‘prediction error’ and not the ‘training error.’ Here’s why. Cross validation actually splits your data into pieces.
What is k-folds cross validation?
K-Folds Cross Validation: K-Folds technique is a popular and easy to understand, it generally results in a less biased model compare to other methods. Because it ensures that every observation from the original dataset has the chance of appearing in training and test set. This is one among the best approach if we have a limited input data.
How many percentage splits can you do in cross validation?
The classic approach is to do a simple 80\%-20\% split, sometimes with different values like 70\%-30\% or 90\%-10\%. In cross-validation, we do more than one split. We can do 3, 5, 10 or any K number of splits. Those splits called Folds, and there are many strategies we can create these folds with.
What is the difference between training set and validation set?
The training set is used to train the model, and the validation/test set is used to validate it on data it has never seen before. The classic approach is to do a simple 80\%-20\% split, sometimes with different values like 70\%-30\% or 90\%-10\%.