K-fold cross-validation is a technique used to assess the performance of a model. It's particularly helpful for estimating how well a model will generalize to new, unseen data. The process involves dividing the dataset into 'k' subsets or folds of approximately equal size. Here's a breakdown of the steps:
1. Dataset Splitting:
The dataset is divided into 'k' equal-sized subsets or folds. For instance, if you have 1,000 samples and choose 'k' as 5, each fold will contain 200 samples.
2. Iterative Training and Evaluation:
The model is trained 'k' times. In each iteration, a different fold is used as the validation set, and the remaining folds are used for training. For example:
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Iteration 1: Fold 1 as validation, Folds 2 to k for training
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Iteration 2: Fold 2 as validation, Folds 1 and 3 to k for training
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Iteration 3: Fold 3 as validation, Folds 1 and 2, and 4 to k for training
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... and so on until all folds have been used as a validation set.
3. Performance Evaluation:
After each iteration, the model's performance is evaluated using a chosen metric (e.g. accuracy, precision, recall, etc.) on the validation set. The performance metrics from each iteration are averaged or combined to give an overall estimate of the model's performance.
4. Aggregation of Metrics:
The performance metrics (e.g. accuracy scores) from each iteration are averaged or combined to provide an overall assessment of the model's performance. This aggregated metric represents the model's expected performance on unseen data.
Advantages of K-fold cross-validation over a simple train/test split
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Better Use of Data: K-fold cross-validation makes better use of the available data as each sample is used for both training and validation.
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Reduced Variance in Performance Estimation: It provides a more reliable estimate of model performance by reducing the variance associated with a single train/test split.
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Generalization: It helps in understanding how the model performs on different subsets of the data, hence assessing its generalization capability.
Choosing the value of 'k'
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Higher 'k' Values: Using a higher 'k' value (e.g. 10 or more) results in smaller validation sets, which may lead to lower bias in the performance estimate but higher computational cost.
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Lower 'k' Values: Using a lower 'k' value (e.g. 3 or 5) reduces computational expense but may lead to a higher bias in the performance estimate due to smaller validation sets.
In practical scenarios
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For large datasets, higher 'k' values can be computationally expensive.
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When the dataset is small, a higher 'k' might not provide enough data in each fold for robust model training.
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Generally, values like 5 or 10 are commonly used as they strike a balance between computational efficiency and reliable performance estimation.
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