  We explores the Pantheon+SH0ES dataset to identify patterns that can
discriminate between different cosmological models. We focus on determining
whether the behaviour of dark energy is consistent with the standard
$\Lambda$CDM model or suggests novel cosmological features. The central goal is
to evaluate the robustness of the $\Lambda$CDM model compared with other dark
energy models, and to investigate whether there are deviations that might
indicate new cosmological insights. The study takes into account a data-driven
approach, using both traditional statistical methods and machine learning
techniques. Initially, we evaluate six different dark energy models using
traditional statistical methods like Markov Chain Monte Carlo (MCMC), Static
and Dynamic Nested Sampling to infer the cosmological parameters. Subsequently,
we adopt a machine learning approach, developing a regression model to compute
the distance modulus of each supernova, expanding the feature set to 74
statistical features. Traditional statistical analysis confirms that the
$\Lambda$CDM model is robust, yielding expected parameter values. Other models
show deviations, with the Generalised and Modified Chaplygin Gas models
performing poorly. In the machine learning analysis, feature selection
techniques, particularly Boruta, significantly improve model performance. In
particular, models initially considered weak (Generalised/Modified Chaplygin
Gas) show significant improvement after feature selection. The study
demonstrates the effectiveness of a data-driven approach to cosmological model
evaluation. The $\Lambda$CDM model remains robust, while machine learning
techniques, in particular feature selection, reveal potential improvements in
alternative models which could be relevant for new observational campaigns like
the recent DESI survey.
