Cross-validation is a statistical method used to estimate the skill of machine learning models. It is commonly used in applied machine learning to compare and select a model for a given predictive modeling problem because it is easy to understand, easy to implement, and results in skill estimates that generally have a lower bias than other methods.

Here's how it works:

1. You split your data into 'k' groups or folds (hence the name 'k-fold cross-validation'). For example, you could split your data into 5 groups or folds.

2. For each unique group:
- Take the group as a test data set.
- Take the remaining groups as a training data set.
- Fit a model on the training set and evaluate it on the test set.
- Retain the evaluation score and discard the model.

3. The result of this method is often given as the mean of the model skill scores. It is also an efficient use of data as each observation in your data sample is used both in the test and train dataset.

Cross-validation is primarily used in applied machine learning to estimate the skill of a machine learning model on unseen data. That is, to use a limited sample in order to estimate how the model is expected to perform in general when used to make predictions on data not used during the training of the model.

Common cross-validation techniques include:

1. Train/Test Split: Taken to one extreme, we could have a single train/test split where we build our model on the training data and evaluate it on the test data.

2. k-fold Cross-Validation: The most common technique, which I explained above.

3. Stratified k-fold Cross-Validation: This is a variation of k-fold which returns stratified folds: each set contains approximately the same percentage of samples of each target class as the complete set.

4. Leave One Out Cross-Validation (LOOCV): Taken to another extreme, we could have a separate train/test split for each observation in our data sample. This is called leave-one-out cross-validation, or LOOCV for short.

5. Repeated Random Test-Train Splits: This is another variation on k-fold cross-validation that creates a random split of the data like the train/test split described above, but repeats the process of splitting and evaluation of the algorithm multiple times, like cross-validation.

These techniques, among others, are often used as they provide a more robust estimate of the model skill on unseen data, compared to other methods.

Question

Cross-validation is a statistical method used to estimate the skill of machine learning models. It is commonly used in applied machine learning to compare and select a model for a given predictive modeling problem because it is easy to understand, easy to implement, and results in skill estimates that generally have a lower bias than other methods.

Here's how it works:

1. You split your data into 'k' groups or folds (hence the name 'k-fold cross-validation'). For example, you could split your data into 5 groups or folds.

2. For each unique group:
   - Take the group as a test data set.
   - Take the remaining groups as a training data set.
   - Fit a model on the training set and evaluate it on the test set.
   - Retain the evaluation score and discard the model.

3. The result of this method is often given as the mean of the model skill scores. It is also an efficient use of data as each observation in your data sample is used both in the test and train dataset.

Cross-validation is primarily used in applied machine learning to estimate the skill of a machine learning model on unseen data. That is, to use a limited sample in order to estimate how the model is expected to perform in general when used to make predictions on data not used during the training of the model.

Common cross-validation techniques include:

1. Train/Test Split: Taken to one extreme, we could have a single train/test split where we build our model on the training data and evaluate it on the test data.

2. k-fold Cross-Validation: The most common technique, which I explained above.

3. Stratified k-fold Cross-Validation: This is a variation of k-fold which returns stratified folds: each set contains approximately the same percentage of samples of each target class as the complete set.

4. Leave One Out Cross-Validation (LOOCV): Taken to another extreme, we could have a separate train/test split for each observation in our data sample. This is called leave-one-out cross-validation, or LOOCV for short.

5. Repeated Random Test-Train Splits: This is another variation on k-fold cross-validation that creates a random split of the data like the train/test split described above, but repeats the process of splitting and evaluation of the algorithm multiple times, like cross-validation.

These techniques, among others, are often used as they provide a more robust estimate of the model skill on unseen data, compared to other methods.

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