ClusterPerformance

Evaluates and compares a clustering model’s performance on training and testing datasets using multiple defined metrics.

Purpose: This metric, ClusterPerformance, evaluates the performance of a clustering model on both the training and testing datasets. It assesses how well the model defines, forms, and distinguishes clusters of data.

Test Mechanism: The metric is applied by first predicting the clusters of the training and testing datasets using the clustering model. Next, performance metrics, defined in the method metric_info(), are calculated against the true labels of the datasets. The results for each metric for both datasets are then collated and returned in a summarized table form listing each metric along with its corresponding train and test values.

Signs of High Risk: - High discrepancy between the performance metric values on the training and testing datasets. This could signify problems such as overfitting or underfitting. - Low performance metric values on the training and testing datasets. There might be a problem with the model itself or the chosen hyperparameters. - If the model’s performance deteriorates consistently across different sets of metrics, this may suggest a broader issue with the model or the dataset.

Strengths: - Tests the model’s performance on both the training and testing datasets, which helps to identify issues such as overfitting or underfitting. - Allows for a broad range of performance metrics to be used, thus providing a comprehensive evaluation of the model’s clustering capabilities. - Returns a summarized table, which makes it easy to compare the model’s performance across different metrics and datasets.

Limitations: - The method metric_info() needs to be properly overridden in a subclass for this class to be used, and the metrics to be used must be manually defined. - The performance metrics are calculated on predicted cluster labels, so the metric may not capture the model’s performance well if the clusters are not well separated or if the model has difficulties with certain kinds of clusters. - Doesn’t consider the computational and time complexity of the model. While the model may perform well in terms of the performance metrics, it might be time or resource-intensive. This metric does not account for such scenarios. - Because the comparison is binary (train and test), it might not capture scenarios where the performance changes drastically under different circumstances or categories within the dataset.