First principles datasets (#181)

* First principles datasets

Data comes from two symbolic regression repos:
- Miles Cranmer's PySR: https://github.com/MilesCranmer/PySR
- Etienne Russeil et al.'s MvSR: https://github.com/erusseil/MvSR-analysis

They are all datasets that have a first-principle equation
derived from data and used in their respective papers
to show how symbolic regression has the potential of retrieving
the original equation when only observational data is available.

While some of them have just a few samples and  others are synthetically
generated, they are challenging for symbolic regression methods and
can be used to evaluate these algorithms.

The idea of pushing them into PMLB is to help other users to
quickly set up experiments with the data.

I still need to write proper metadata for them.

* Re-generated broken datasets

CI was failing to parse the contents of these specific ones.

* update dataset files

Created by https://github.com/gAldeia/pmlb/actions/runs/11616806556\nfrom f23672c on 2024-10-31

* New metadata

* Updated summary

* update dataset files

Created by https://github.com/gAldeia/pmlb/actions/runs/13434894123\nfrom bdc87c8 on 2025-02-20

* Fix typo

* update dataset files

Created by https://github.com/gAldeia/pmlb/actions/runs/13465733857\nfrom a226e6b on 2025-02-21

---------

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>

Author: gAldeia

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datasets/auto_insurance_losses/README.md

@@ -1,2 +1,2 @@
 dataset	n_instances	n_features	n_binary_features	n_categorical_features	n_continuous_features	endpoint_type	n_classes	imbalance	task
-auto_insurance_losses	164	24	3	7	14	continuous	51	0.00844289113622844	regression
+auto_insurance_losses	164	24	3	6	15	categorical	51.0	0.008442891136228436	regression

datasets/auto_insurance_losses/summary_stats.tsv

@@ -1,2 +1,2 @@
 dataset	n_instances	n_features	n_binary_features	n_categorical_features	n_continuous_features	endpoint_type	n_classes	imbalance	task
-auto_insurance_price	201	23	4	6	13	continuous	186	0.000343181229792947	regression
+auto_insurance_price	201	23	4	5	14	categorical	186.0	0.0003431812297929476	regression

datasets/auto_insurance_price/README.md

@@ -1,2 +1,2 @@
 dataset	n_instances	n_features	n_binary_features	n_categorical_features	n_continuous_features	endpoint_type	n_classes	imbalance	task
-auto_insurance_symboling	205	24	4	6	14	ordinal	6	0.0755788221296847	classification
+auto_insurance_symboling	205	24	4	5	15	categorical	6.0	0.07557882212968471	classification

datasets/auto_insurance_price/summary_stats.tsv

@@ -1,2 +1,2 @@
 dataset	n_instances	n_features	n_binary_features	n_categorical_features	n_continuous_features	endpoint_type	n_classes	imbalance	task
-breast_cancer_wisconsin_diagnostic	569	30	0	0	30	binary	2	0.064939878490615	classification
+breast_cancer_wisconsin_diagnostic	569	30	0	0	30	categorical	2.0	0.06493987849061501	classification