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Machine learning approach for mapping the stable orbits around planets

T0 review · 3 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read A gradient-boosted tree classifier trained on 100,000 three-body simulations predicts whether test-particle orbits around a planet remain stable for 10,000 planetary periods, reaching 98.48% accuracy and producing full stability maps in…

desk verdict A solid ML-surrogate paper for mapping short-term orbital survival around planets, but the 10^4-orbit stability label should be stated plainly in the title and abstract. read the letter →

arxiv 2412.04568 v1 pith:ZPBFPO3N submitted 2024-12-05 astro-ph.EP astro-ph.IMcs.LGphysics.comp-ph

Tiago F. L. L. Pinheiro , Rafael Sfair , Giovana Ramon This is my paper · ORCID
classification astro-ph.EPastro-ph.IMcs.LGphysics.comp-ph
keywords machinelearningorbitalstabilitythree-bodyproblemexoplanetsXGBoostmapsN-bodysimulationssatelliteandringsystems
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper claims that a machine-learning classifier can replace expensive numerical integration when mapping which orbits around a planet remain stable. The authors train a gradient-boosted tree model on 100,000 dimensionless three-body simulations, with stability defined as surviving 10,000 planetary orbital periods without collision or ejection. On held-out test data the model reaches 98.48% accuracy, 94% recall and precision for stable orbits, and 99% for unstable orbits; it also reproduces published stability maps for exomoon, ring, and Saturn-satellite systems with accuracies between 86% and 98%. A full stability map is produced in about 0.5 second, versus days for direct integration, so the method turns stability mapping into a fast screening tool.

What carries the argument

The load-bearing object is a labeled dataset of $10^{5}$ dimensionless three-body simulations, each described by nine features: system mass ratio, particle semi-major axis, eccentricity, inclination, argument of pericenter, longitude of node, and true anomaly, plus planet eccentricity and true anomaly. Simulations are integrated with a high-accuracy numerical integrator until particle collision with the planet, ejection to a hyperbolic orbit, or the 10,000-period horizon, and each initial condition is labeled stable or unstable accordingly. The model that carries the argument is XGBoost, a gradient-boosted decision-tree ensemble, tuned with a decision threshold of 0.4 and selected hyperparameters; it maps the nine-dimensional feature space to a stability probability, and the paper uses its built-in feature-importance scores to show that particle semi-major axis, eccentricity, and inclination dominate the classification.

What would settle it

Re-run the 20,000 test cases with the integration time extended from $10^{4}$ to $10^{6}$ planetary orbital periods and count how many orbits the model labels stable become unstable; if that fraction is more than a few percent, the 98.48% accuracy is an artifact of the finite integration horizon. A cheaper check is to compare the model's boundary predictions against fresh high-resolution integrations for a dense grid of retrograde inclinations near 180 degrees, the region the paper notes is most sensitive.

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Extended reading notes

Core claim

The paper's central claim is that orbital stability in the star-planet-test-particle three-body problem is a learnable function of nine initial-condition features, and that a tuned gradient-boosted tree model extracts that function accurately enough to replace numerical integration for map generation. Training on 100,000 dimensionless simulations in which 'stable' means the particle neither collided with the planet nor was ejected within 10,000 planetary orbital periods, the best model reaches 98.48% test accuracy and an AUC of 0.9978, with recalls and precisions of 94% for stable particles and 99% for unstable ones. The same model reproduces published stability maps at accuracies of 95.86% for the prograde case and 86.24% for the retrograde case of Domingos et al. (2006), 93.68% for the PDS110b ring system, and 97.57% for Saturn's Inuit satellites, while producing a 10,000-point map in roughly 0.5 second. The authors present this as a method that makes stability mapping a fast screening tool; the finite-horizon definition of stability is the main caveat they state explicitly.

Load-bearing premise

The load-bearing premise is that 'stable' can be defined as surviving 10,000 planetary orbital periods; the paper itself notes in Section 6.2 that some particles labeled stable might become unstable with longer integration times, so every accuracy figure inherits this finite-horizon definition.

Editorial extensions

If this is right

  • Stability maps around a star-planet system can be generated in seconds instead of days, making parameter surveys of many systems feasible.
  • Within the trained mass-ratio and orbital-element space, the same model applies to planet-satellite pairs, planetary ring systems, and binary minor planets without new integrations.
  • Feature-importance rankings identify particle semi-major axis, eccentricity, and inclination as the dominant stability parameters, with planet eccentricity secondary, which can guide future feature engineering.
  • The planned public web interface will let researchers obtain stability predictions for their own systems without running N-body simulations.
  • Discrepancies between ML maps and analytical boundaries, for example in the retrograde case, point to specific regions that deserve targeted numerical study.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The advertised speedup applies to prediction after training; training itself required the 100,000 simulations, so the practical gain is for repeated map generation within the already-simulated parameter space.
  • Augmenting the training set with survival labels at 10^5 and 10^6 planetary periods would convert the binary classifier into a time-dependent survival predictor and directly test whether the finite-horizon definition drives the reported accuracy.
  • The paper's own remark about sensitivity near i=180 suggests that an active-learning scheme adding extra simulations near that boundary could reduce the misclassifications it reports there.
  • A natural hybrid use of the model is to pre-screen candidate exomoon orbits around the exoplanet catalogue and reserve full N-body integration for the uncertain boundary strips.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The manuscript presents a supervised machine-learning surrogate for three-body stability maps in the star-planet-test-particle problem. The authors generate 100,000 dimensionless numerical integrations with rebound/IAS15, label each initial condition as stable or unstable according to survival for 10^4 planetary orbital periods, and train eight classifiers on nine orbital features. After hyperparameter and threshold tuning, an XGBoost model achieves 98.48% accuracy on a held-out 20% test set, with stable-class precision and recall of 94% and unstable-class values of about 99%. The model is then applied to reproduce published maps (Domingos et al. 2006), a PDS110b ring scenario, and Saturn's Inuit satellites, with reported accuracies of 86 to 98%, and to generate maps in about 0.5 seconds. The paper is clearly written and the workflow is easy to follow, but the finite-time definition of the ground-truth label is a central limitation that the manuscript itself acknowledges in Section 6.2.

Significance. Within the defined task of classifying finite-time survival for 10^4 planetary periods, the paper demonstrates a fast, internally consistent surrogate. The held-out evaluation, the threshold and hyperparameter tuning, and the comparisons against external systems are genuine strengths, and the feature-importance ranking is physically sensible. If the 10^4-period survival criterion is accepted, the inference-only speed-up of roughly five orders of magnitude is substantial and the comparison maps are informative. The significance for true long-term dynamical stability is, however, qualified by the ground-truth definition: every accuracy number and map inherits the finite-time proxy, so the title and abstract claims about 'stable orbits' and 'stable regions' overstate what the model has been validated to predict. The paper would be strengthened by a convergence test over integration time and by a clear statement that the maps are survival maps for the stated timescale.

major comments (3)
  1. [Sec. 3 / Sec. 6.2] The ground truth for all 100,000 training examples, and therefore for every reported metric and comparison map, is survival for 10^4 planetary orbital periods, not dynamical stability in a longer-term sense. The paper itself concedes in Section 6.2 that 'some of these stable particles might become unstable with longer integration times.' Because this finite-time proxy enters before training, the 98.48% test accuracy and the external comparisons in Sections 6.1 to 6.3 cannot be read as validating the model for long-term stability. Please either rescope the wording of the title, abstract, and conclusions to 'survival over 10^4 orbital periods' or add a convergence experiment, for example by relabeling with 10^3, 10^5, and 10^6-period integrations at boundary regions and checking whether the empirical stability boundary moves.
  2. [Sec. 5.1] The headline metrics, including the 98.48% accuracy and the 94% stable-class recall and precision, are point estimates from a single 20% holdout split. The model-selection comparison in Figure 5 relies on differences as small as about 1% between algorithms, and the claim that XGBoost is the best-performing model would be much more robust with bootstrap confidence intervals or repeated cross-validation. Please report uncertainty estimates for the test-set metrics, particularly for the minority stable class.
  3. [Sec. 6.1 / Sec. 6.2] The 'numerical simulation results' used as the reference in the Domingos et al., PDS110b, and Saturn comparisons are not described with the same level of detail as the training dataset. It is not stated whether these reference maps were produced with the same 10^4-period stopping time, the same collision radius prescription, and the same ejection criteria as the training labels. Without this specification, the comparison accuracies in Figures 9, 11, and 13 cannot be fully audited. Please state explicitly which integrator, stopping criterion, and stability criteria were used for each comparison map.
minor comments (5)
  1. [Sec. 5.1 / Fig. 5] The sentence stating that XGBoost 'attains the same percentages for precision and recall' is ambiguous, because the immediately preceding numbers refer to LightGBM and Random Forest while the abstract quotes 94% for XGBoost; please give the exact XGBoost values in the text so that Figure 5 and the abstract agree.
  2. [Sec. 3 / Sec. 7] The collision and ejection percentages are reported as 53.56% and 46.44% of unstable particles in Section 3 but as 47.22% and 40.95% in Section 7; these are consistent only if the denominators differ, so please state the denominator explicitly in both places.
  3. [Sec. 5] The hyperparameter grids for the Grid and Random searches, the number of cross-validation repeats, and the random seeds are not specified; these details are important for interpreting the small performance differences between the tree-based ensembles in Figure 5.
  4. [Sec. 7] The paper does not currently provide code, data, or trained-model artifacts, and the web interface is described only as forthcoming, making exact reproduction of the headline results difficult; please include a reproducibility statement or a plan for releasing these artifacts.
  5. [Sec. 3] There is a small typographical error in the sentence 'We labeled each initial condition with the numerical results as either stable or un unstable system'; 'un unstable' should be 'unstable'.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the XGBoost accuracy claim is a held-out supervised-learning evaluation, and the overlapping-author references are comparison benchmarks rather than load-bearing premises.

full rationale

The paper's central claim is that a classifier trained on 100,000 three-body simulations predicts the training-label outcome on a held-out test set with 98.48% accuracy. Section 3 defines the labels directly from numerical outcomes: "We labeled each initial condition with the numerical results as either stable or un unstable system," and Section 5.1 evaluates on data not used for training: "The final dataset consists of 100,000 simulations, divided into three subsets: 80% for training and validation data and 20% for testing data." This is a standard out-of-sample evaluation, not a fitted parameter renamed as a prediction. The validation maps in Section 6 are generated with the same numerical integrator and the same 10^4-period stability criterion as the training labels, so they test self-consistency of the surrogate rather than providing an independent physical benchmark; however, no equation or fitted value is reused by construction, and the comparison with the analytical expression of Domingos et al. (2006) is external. The only overlapping-author reference, Pinheiro & Sfair (2021), supplies boundary parameters and a prior map for comparison, not justification for the model's accuracy. The finite-time survival definition is a stated limitation, which the authors concede in Section 6.2: "Our numerical simulations cover a time span of 10^4 orbital periods; however, some of these stable particles might become unstable with longer integration times." That is a correctness and generalization caveat about the ground-truth label, not a circular derivation step. No step of the paper reduces to its own inputs by definition or by self-citation chain.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The paper introduces no new physical entities. Its load-bearing assumptions are the finite-time stability definition, the representativeness of random uniform sampling, the smooth-interpolation assumption, and the collision-radius proxy. The only fitted numbers are standard ML hyperparameters and the decision threshold, which are tuned to the validation set rather than derived from physics.

free parameters (4)
  • XGBoost learning rate eta = 0.2
    Tuned through cross-validation; scales the contribution of each tree and directly affects model accuracy.
  • XGBoost max_depth = 8
    Tuned through cross-validation; controls tree complexity and overfitting behavior.
  • XGBoost scale_pos_weight = 0.4
    Tuned to handle the imbalanced stable versus unstable classes; changes the effective loss weighting.
  • Classification threshold = 0.4
    Chosen from the ROC curve rather than the default 0.5; directly affects precision and recall for each class.
assumptions (5)
  • domain assumption Newtonian point-mass three-body dynamics integrated with REBOUND and IAS15 accurately represent orbital stability for these systems.
    Section 3 describes all simulations as three-body integrations with REBOUND and IAS15; no comparison to other integrators or to non-gravitational effects is provided.
  • domain assumption A particle is 'stable' if it survives 10,000 planetary orbital periods, and 'unstable' if it collides with the planet or is ejected (eccentricity reaches 1 or semi-major axis exceeds 1).
    This finite-time definition is the ground truth for all labels; Section 6.2 acknowledges that some labeled-stable particles may become unstable on longer timescales.
  • domain assumption Uniform random sampling over the chosen parameter ranges is representative of exoplanet satellite and ring systems.
    The mass ratio is restricted to [1e-5, 1e-2] based on an exoplanet catalog survey, and orbital elements are sampled uniformly; the generalization claims depend on this coverage.
  • domain assumption The stability boundary is smooth enough that a classifier trained on random samples can interpolate to dense map grids.
    Maps are generated on grids with steps of 0.1 planet radii and 0.01 in eccentricity while training samples are random; the retrograde boundary errors in Section 6.1 suggest this assumption is imperfect.
  • domain assumption The collision radius model rc = 0.05 times the Hill radius at pericenter is an adequate proxy for physical collisions.
    Section 3 sets rc from a survey of known exoplanets and labels particles that reach this radius as unstable; changing the radius model would change the labels.

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Cite this review

Pith. "Pith review of Machine learning approach for mapping the stable orbits around planets." pith.science (2026). https://pith.science/paper/ZPBFPO3N

@misc{pith2026241204568,
  author       = {Pith},
  title        = {Pith review of: Machine learning approach for mapping the stable orbits around planets},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZPBFPO3N}},
  note         = {Machine review of arXiv:2412.04568}
}
read the original abstract

Numerical N-body simulations are commonly used to explore stability regions around exoplanets, offering insights into the possible existence of satellites and ring systems. This study aims to utilize Machine Learning (ML) techniques to generate predictive maps of stable regions surrounding a hypothetical planet. The approach can also be extended to planet-satellite systems, planetary ring systems, and other similar configurations. A dataset was generated using 10^5 numerical simulations, each incorporating nine orbital features for the planet and a test particle in a star-planet-test particle system. The simulations were classified as stable or unstable based on stability criteria, requiring particles to remain stable over a timespan equivalent to 10,000 orbital periods of the planet. Various ML algorithms were tested and fine-tuned through hyperparameter optimization to determine the most effective predictive model. Tree-based algorithms showed comparable accuracy in performance. The best-performing model, using the Extreme Gradient Boosting (XGBoost) algorithm, achieved an accuracy of 98.48%, with 94% recall and precision for stable particles and 99% for unstable particles. ML algorithms significantly reduce the computational time required for three-body simulations, operating approximately 100,000 times faster than traditional numerical methods. Predictive models can generate entire stability maps in less than a second, compared to the days required by numerical simulations. The results from the trained ML models will be made accessible through a public web interface, enabling broader scientific applications.

Figures

Figures reproduced from arXiv: 2412.04568 by the authors.

Figure 1
Figure 1. Flowchart illustrating the workflow of this paper. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. The ROC curve representation of three hypothetical mod [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Histogram of the number of confirmed planets in units of their Hill radius from a survey consisting 4,150 different planets (Schneider et al. 2011). The planet eccentricity (ep) and true anomaly (fp) were ran￾domly uniformly sampled within the specified ranges: ep = [0− 0.99] and fp = [0◦ − 360◦ ]. The remaining orbital elements for the planet inclination (ip), arguments of pericenter (ϖp), and lon￾gitude of the nod… view at source ↗
Figures from the paper (9 more)
Figure 4
Figure 4. Figure 4: Histogram of the distribution of stable (orange) and un [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]
Figure 5
Figure 5. Figure 5: The performance of the best model among the five [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 6
Figure 6. Figure 6: Confusion matrices for the best models of five ML algo [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]
Figure 7
Figure 7. Figure 7: Feature importance derived from the XGBoost model. [PITH_FULL_IMAGE:figures/full_fig_p006_7.png]
Figure 8
Figure 8. Figure 8: Comparison of numerical simulations (upper panels) and ML predicted outcomes (lower panels) for stable (orange) and [PITH_FULL_IMAGE:figures/full_fig_p008_8.png]
Figure 9
Figure 9. Figure 9: Performance comparison between numerical simulations [PITH_FULL_IMAGE:figures/full_fig_p008_9.png]
Figure 10
Figure 10. Figure 10: Stability maps for PDS110b. Top panel: Numerical sim [PITH_FULL_IMAGE:figures/full_fig_p009_10.png]
Figure 12
Figure 12. Figure 12: Stability maps for Saturn’s Inuit satellites. Top panel: [PITH_FULL_IMAGE:figures/full_fig_p009_12.png]
Figure 13
Figure 13. Figure 13: Performance metrics for the Saturn’s Inuit satellites pre [PITH_FULL_IMAGE:figures/full_fig_p010_13.png]

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Forward citations

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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Reference graph

Works this paper leans on

40 extracted references · 33 canonical work pages · cited by 1 Pith paper

  1. [1]

    , " * write output.state after.block = add.period write newline

    ENTRY address archiveprefix author booktitle chapter edition editor howpublished institution eprint journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.all := #1 ...

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in " " * FUNCTION format....

  3. [3]

    2021, Artif

    Bent \'e jac, C., Cs \"o rg o , A., & Mart \' nez-Mu \ n oz, G. 2021, Artif. Intell. Rev., 54, 1937

    work page 2021

  4. [4]

    1996, Mach

    Breiman, L. 1996, Mach. Learn., 24, 123

    work page 1996

  5. [5]

    2001, Mach

    Breiman, L. 2001, Mach. Learn., 45, 5

    2001

  6. [6]

    2023 a , FrASS, 10, 1196223

    Carruba, V., Aljbaae, S., Carit \'a , G., et al. 2023 a , FrASS, 10, 1196223

    work page 2023

  7. [7]

    2023 b , FrASS, 10

    Carruba, V., Aljbaae, S., Caritá, G., et al. 2023 b , FrASS, 10

    work page 2023

  8. [8]

    V., Bowyer, K

    Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. 2002, JAIR, 16, 321

    work page 2002

Show all 40 references
  1. [9]

    2004, in IJCNN, Vol

    Chen, P.-W., Wang, J.-Y., & Lee, H.-M. 2004, in IJCNN, Vol. 3, IEEE, 2035--2040

    2004

  2. [10]

    & Guestrin, C

    Chen, T. & Guestrin, C. 2016, in In Proc. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785--794

    2016

  3. [11]

    2021, PNAS, 118, e2026053118

    Cranmer, M., Tamayo, D., Rein, H., et al. 2021, PNAS, 118, e2026053118

    2021

  4. [12]

    2011, Random Forests

    Cutler, A., Cutler, D., & Stevens, J. 2011, Random Forests. Vol. 45. 157--176

    2011

  5. [13]

    C., Winter, O

    Domingos, R. C., Winter, O. C., & Yokoyama, T. 2006, MNRAS, 373, 1227

    2006

  6. [14]

    Friedman, J. H. 2001, AnSta, 1189

    2001

  7. [15]

    2011, Stat

    Fushiki, T. 2011, Stat. Comput., 21, 137

    2011

  8. [16]

    O'Reilly Media, Inc

    G \'e ron, A. 2022, Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow (" O'Reilly Media, Inc.")

    2022

  9. [17]

    2005, in ICIC, Springer, 878--887

    Han, H., Wang, W.-Y., & Mao, B.-H. 2005, in ICIC, Springer, 878--887

    2005

  10. [18]

    A., & Li, S

    He, H., Bai, Y., Garcia, E. A., & Li, S. 2008, in IJCNN, Ieee, 1322--1328

    2008

  11. [19]

    Holman, M. J. & Wiegert, P. A. 1999, AJ, 117, 621

    1999

  12. [20]

    1967, MNRAS, 136, 245

    Hunter, R. 1967, MNRAS, 136, 245

    1967

  13. [21]

    M., & Semary, N

    Ibrahim, M., AbdelRaouf, H., Amin, K. M., & Semary, N. 2023, IJCI, 10, 36

    2023

  14. [22]

    2017, Adv Neural Inf Process Syst, 30

    Ke, G., Meng, Q., Finley, T., et al. 2017, Adv Neural Inf Process Syst, 30

    2017

  15. [23]

    & Sheshadri, H

    Kumar, M. & Sheshadri, H. 2012, IJCA, 44, 1

    2012

  16. [24]

    & Kipping, D

    Lam, C. & Kipping, D. 2018, MNRAS, 476, 5692

    2018

  17. [25]

    1997, McGraw Hill

    Mitchell, T. 1997, McGraw Hill

    1997

  18. [26]

    2020, in ICICS, IEEE, 243--248

    Mohammed, R., Rawashdeh, J., & Abdullah, M. 2020, in ICICS, IEEE, 243--248

    2020

  19. [27]

    Neto, E. V. & Winter, O. 2001, AJ, 122, 440

    2001

  20. [28]

    Pinheiro, T. F. & Sfair, R. 2021, A&A, 652, A149

    2021

  21. [29]

    Quinlan, J. R. 1986, Mach. Learn., 1, 81

    1986

  22. [30]

    & Spiegel, D

    Rein, H. & Spiegel, D. S. 2015, MNRAS, 446, 1424

    2015

  23. [31]

    & Kenworthy, M

    Rieder, S. & Kenworthy, M. A. 2016, A&A, 596, A9

    2016

  24. [32]

    2011, A&A, 532, A79

    Schneider, J., Dedieu, C., Le Sidaner, P., Savalle, R., & Zolotukhin, I. 2011, A&A, 532, A79

    2011

  25. [33]

    & Ben-David, S

    Shalev-Shwartz, S. & Ben-David, S. 2014, Understanding machine learning: From theory to algorithms (Cambridge university press)

    2014

  26. [34]

    Stehman, S. V. 1997, RSEnv, 62, 77

    1997

  27. [35]

    2020, PNAS, 117, 18194

    Tamayo, D., Cranmer, M., Hadden, S., et al. 2020, PNAS, 117, 18194

    2020

  28. [36]

    2016, ApJL, 832, L22

    Tamayo, D., Silburt, A., Valencia, D., et al. 2016, ApJL, 832, L22

    2016

  29. [37]

    & Glynn, K

    Wade, C. & Glynn, K. 2020, Hands-On Gradient Boosting with XGBoost and scikit-learn: Perform accessible machine learning and extreme gradient boosting with Python (Packt Publishing Ltd)

    2020

  30. [38]

    J., Mueller, A

    Weerts, H. J., Mueller, A. C., & Vanschoren, J. 2020, arXiv

    2020

  31. [39]

    2015, Comput

    Zheng, Z., Cai, Y., & Li, Y. 2015, Comput. Info., 34, 1017

    2015

  32. [40]

    2016, Big Data Res., 5, 2

    Zou, Q., Xie, S., Lin, Z., Wu, M., & Ju, Y. 2016, Big Data Res., 5, 2

    2016

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