What Makes a Satisfying Life? Prediction and Interpretation with Machine‐Learning Algorithms Article Swipe
Niccolò Gentile
,
Michela Bia
,
Andrew E. Clark
,
Conchita D’Ambrosio
,
Alexandre Tkatchenko
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1111/roiw.70003
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1111/roiw.70003
Machine Learning (ML) methods are increasingly being used across a variety of fields, and have led to the discovery of intricate relationships between variables. We here apply ML methods to predict and interpret life satisfaction using data from the UK British Cohort Study. We discuss the application of first Penalized Linear Models and then one non‐linear method, Random Forests. We present two key model‐agnostic interpretative tools for the latter method: Permutation Importance and Shapley Values. With a parsimonious set of explanatory variables, neither Penalized Linear Models nor Random Forests produce major improvements over the standard Non‐penalized Linear Model. However, once we consider a richer set of controls these methods do produce a non‐negligible improvement in predictive accuracy. Although marital status, and emotional health continue to be the most‐important predictors of life satisfaction, as in the existing literature, gender becomes insignificant in the non‐linear analysis.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1111/roiw.70003
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/roiw.70003
- OA Status
- bronze
- References
- 38
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408883368
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Raw OpenAlex JSON
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https://openalex.org/W4408883368Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1111/roiw.70003Digital Object Identifier
- Title
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What Makes a Satisfying Life? Prediction and Interpretation with Machine‐Learning AlgorithmsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-03-25Full publication date if available
- Authors
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Niccolò Gentile, Michela Bia, Andrew E. Clark, Conchita D’Ambrosio, Alexandre TkatchenkoList of authors in order
- Landing page
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https://doi.org/10.1111/roiw.70003Publisher landing page
- PDF URL
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/roiw.70003Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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bronzeOpen access status per OpenAlex
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/roiw.70003Direct OA link when available
- Concepts
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Interpretation (philosophy), Computer science, Economics, Machine learning, Artificial intelligence, Algorithm, Programming languageTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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38Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2911964244, https://openalex.org/W2800748344, https://openalex.org/W4255153270, https://openalex.org/W2141463165, https://openalex.org/W2950120180, https://openalex.org/W2078205970, https://openalex.org/W2571495779, https://openalex.org/W2137918053, https://openalex.org/W1978050391, https://openalex.org/W3125787270, https://openalex.org/W4294541781, https://openalex.org/W3125114535, https://openalex.org/W2787894218, https://openalex.org/W4234698323, https://openalex.org/W2487770199, https://openalex.org/W2101462238, https://openalex.org/W3124335630, https://openalex.org/W2551974706, https://openalex.org/W1501830245, https://openalex.org/W2148672604, https://openalex.org/W2890545289, https://openalex.org/W2910705748, https://openalex.org/W4242288480, https://openalex.org/W3116364167, https://openalex.org/W6675354045, https://openalex.org/W6885100702, https://openalex.org/W2487898712, https://openalex.org/W2129888542, https://openalex.org/W3121912189, https://openalex.org/W2135046866, https://openalex.org/W2152204644, https://openalex.org/W2092027953, https://openalex.org/W2122825543, https://openalex.org/W3204438512, https://openalex.org/W1594031697, https://openalex.org/W4300576158, https://openalex.org/W4299400367, https://openalex.org/W2101234009 |
| referenced_works_count | 38 |
| abstract_inverted_index.a | 10, 77, 103, 112 |
| abstract_inverted_index.ML | 28 |
| abstract_inverted_index.UK | 40 |
| abstract_inverted_index.We | 25, 44, 60 |
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| abstract_inverted_index.be | 126 |
| abstract_inverted_index.do | 110 |
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| abstract_inverted_index.one | 55 |
| abstract_inverted_index.set | 79, 105 |
| abstract_inverted_index.the | 18, 39, 46, 68, 94, 127, 135, 142 |
| abstract_inverted_index.two | 62 |
| abstract_inverted_index.(ML) | 3 |
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| abstract_inverted_index.data | 37 |
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| abstract_inverted_index.Cohort | 42 |
| abstract_inverted_index.Linear | 51, 85, 97 |
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| abstract_inverted_index.Models | 52, 86 |
| abstract_inverted_index.Random | 58, 88 |
| abstract_inverted_index.Study. | 43 |
| abstract_inverted_index.across | 9 |
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| abstract_inverted_index.latter | 69 |
| abstract_inverted_index.richer | 104 |
| abstract_inverted_index.British | 41 |
| abstract_inverted_index.Forests | 89 |
| abstract_inverted_index.Machine | 1 |
| abstract_inverted_index.Shapley | 74 |
| abstract_inverted_index.Values. | 75 |
| abstract_inverted_index.becomes | 139 |
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| abstract_inverted_index.fields, | 13 |
| abstract_inverted_index.marital | 119 |
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| abstract_inverted_index.methods | 4, 29, 109 |
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| abstract_inverted_index.status, | 120 |
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| abstract_inverted_index.Forests. | 59 |
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| abstract_inverted_index.Penalized | 50, 84 |
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| abstract_inverted_index.predictors | 129 |
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| abstract_inverted_index.variables. | 24 |
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| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5047611873 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I186903577 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/5 |
| sustainable_development_goals[0].score | 0.6299999952316284 |
| sustainable_development_goals[0].display_name | Gender equality |
| citation_normalized_percentile.value | 0.10591286 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |