k equal-sized subsets cross-validation
K-fold cross-validation technique ensures each subset has an approximately equal number of samples.
Notes
Equal-sized subsets cross-validation is a powerful technique for evaluating machine learning models on limited data samples. By dividing the dataset into k equal-sized subsets and iteratively using one subset as a test set while combining the remaining (k-1) subsets into a new training set, this method provides an accurate assessment of model performance.
TakeAways
- 📌 k Equal-Sized Subsets Cross-Validation ensures that each subset has approximately the same number of samples.
- 💡 This technique helps in evaluating machine learning models accurately on a limited dataset
- 🔍 It is a resampling procedure widely used for assessing ⚡ Model performance
Process
- 📋 Divide the dataset into k equal-sized subsets.
- 🔄 Iterate through the k subsets, taking one subset out as the Data Test set and combining the remaining (k-1) subsets into a new Data Training Set .
- 🏋️ train the model using the new training set and calculate its performance on the test set
- 📈 Repeat steps 1-3 until all subsets have been used once as the test set
Thoughts
- 🧠 Equal-Sized Subsets Cross-Validation helps ensure that each fold has approximately the same number of samples, thus providing a more accurate evaluation of machine learning models on limited datasets.
- Trains the model k times, each time using a different fold as the validation set and the remaining folds for training.