ClusterSizeDistribution

Compares and visualizes the distribution of cluster sizes in model predictions and actual data for assessing clustering model performance.

Purpose: The purpose of the ClusterSizeDistribution metric is to assess the performance of clustering models. It does this by comparing the distribution of cluster sizes in the predictions made by the model and the actual data. Observing the cluster distribution helps gain insights into whether the model’s output aligns well with the actual dataset distribution.

Test Mechanism: The testing mechanism for ClusterSizeDistribution involves first running the clustering model on the training dataset, storing predictions, and comparing these predictions with the actual output. The actual and predicted outputs are then converted into pandas dataframes, which conveniently enables the use of pandas built-in functions to derive cluster size distributions. Two histograms are constructed from this data: one for the actual distribution and one for the predicted distribution. These histograms are then plotted side-by-side for visual comparison.

Signs of High Risk: * Discrepancies between the actual cluster size distribution and the predicted cluster size distribution may indicate high risk. * An irregular distribution of data across clusters in the predicted outcomes points towards an inaccurate prediction model. * A high number of outlier clusters could indicate that the model has trouble correctly grouping data.

Strengths: * ClusterSizeDistribution provides a visual and intuitive way to compare the performance of the clustering model against the actual data. * This metric can effectively reveal where the model might be over- or underestimating cluster sizes. * It works well with any clustering models, making it a versatile comparison tool.

Limitations: * The metric assumes that the actual cluster distribution is optimal, which may not always be the case. * It relies heavily on visual comparison, which might be subjective and may not provide a precise numerical measure of model performance. * The metric might not fully capture other important aspects of clustering such as cluster density, distances between clusters, and the shape of clusters.