Which cross-validation approach can be used for clustering when ground truth labels are available?Review LaterExternal ValidationHoldout ValidationStratified Cross-ValidationShuffle Split Cross-Validation
Question
Which cross-validation approach can be used for clustering when ground truth labels are available?Review LaterExternal ValidationHoldout ValidationStratified Cross-ValidationShuffle Split Cross-Validation
Solution 1
The cross-validation approach that can be used for clustering when ground truth labels are available is External Validation.
Here's a step-by-step explanation:
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External Validation: This method is used when you have the ground truth labels available. It involves comparing the clusters formed by your algorithm with the ground truth labels. The quality of the clustering algorithm is then evaluated based on how well the clusters match with the ground truth labels.
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Holdout Validation, Stratified Cross-Validation, and Shuffle Split Cross-Validation are typically used in supervised learning scenarios where you have input-output pairs. These methods involve splitting the data into training and test sets in different ways.
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Holdout Validation: This involves splitting the data into a training set and a test set. The model is trained on the training set and evaluated on the test set.
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Stratified Cross-Validation: This involves splitting the data in such a way that each fold has the same proportion of samples of each target class as the complete set. It is used in scenarios where the data is imbalanced.
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Shuffle Split Cross-Validation: This involves generating a user defined number of independent train / test dataset splits. Samples are first shuffled and then split into a pair of train and test sets.
In the context of clustering, where the goal is to discover hidden patterns or groups in data, External Validation is the most appropriate method when ground truth labels are available.
Solution 2
The cross-validation approach that can be used for clustering when ground truth labels are available is External Validation.
Here's a step-by-step explanation:
-
External Validation: This method is used when you have the ground truth labels available. It involves comparing the clusters formed by your algorithm with the actual labels to measure the quality of the clustering. This can be done using metrics like Adjusted Rand Index (ARI), Normalized Mutual Information (NMI), etc.
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Holdout Validation: This method involves splitting the data into a training set and a test set. The model is trained on the training set and tested on the test set. However, this method is
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