Motivation: Clustering is an unsupervised learning task with broad applications. Traditional clustering methods often rely on point estimates of model parameters, which can limit their ability to capture uncertainty. Bayesian clustering addresses this by incorporating uncertainty into parameter estimation. However, existing Bayesian inference methods like Markov Chain Monte Carlo and Variational Inference are computationally intensive and can produce biased approximations. To overcome these limitations, we propose Cluster-PFN, a transformer-based model inspired by Prior-Data Fitted Networks. Cluster-PFN simultaneously approximates the posterior distributions over the cluster assignments of individual data points and the total number of clusters for the given dataset in a single forward pass. Our model provides fast and accurate Bayesian clustering, supporting data with up to five features and conditioning on user-specified cluster counts.

Results: Our results demonstrate that Cluster-PFN can predict the number of clusters up to 20% more accurately than standard heuristics. It also outperforms the Bayesian Gaussian Mixture Model using Variational Inference (Bayesian GMM VI), achieving up to 60% higher scores on certain external metrics while being up to 20 times faster during inference. Additionally, Cluster-PFN surpasses both the traditional Gaussian Mixture Model and K-means++ across the same external evaluation metrics.

A learning curve displays the measure of accuracy/error on test data of a machine learning algorithm trained on different amounts of training data. They can be modeled by parametric curve models that help predict accuracy improvement through curve extrapolation methods. However, these learning curves have only been mainly generated from default learning algorithms. Research into tuning the machine learning algorithm and its effect on the learning curve has not been adequately researched. This research aims to look at the influence of hyperparameter tuning on the learning curve. This regards not only how the learning curve shape changes in general but also how different parametric models are affected when a learner undergoes tuning. We experiment with the decision tree and KNeighbors classifier which undergo significant hyperparameter tuning. We find that the tuned learner performs marginally better than the default learner for anchors past 25\% of the data for the majority of the tested datasets. We also observe that the tuned learner displays a smoothing behaviour that makes ill-behaved curves more well-behaved. In terms of the curve fitting, the tuned learners do not uncover any curve models nor does it show any statistical significance, and instead performs very similarly to the default learners.