---
license: cc-by-nc-4.0
library_name: joblib
pipeline_tag: tabular-classification
tags:
- relationships
- gottman
- survival-analysis
- cox-proportional-hazards
- xgboost
- lightgbm
- catboost
- shap
- ensemble
- tabular-classification
- couples
- social-science
datasets:
- mstz/speeddating
- vedastro-org/15000-Famous-People-Marriage-Divorce-Info
metrics:
- roc_auc
- accuracy
- f1
model-index:
- name: relationship-longevity-predictor-v2
  results:
  - task:
      type: tabular-classification
      name: Relationship Longevity Prediction
    dataset:
      name: Speed Dating + Gottman Divorce + Vedastro Marriages (composite)
      type: custom
    metrics:
    - type: roc_auc
      value: 0.8896
      name: AUC-ROC
    - type: accuracy
      value: 0.859
      name: Accuracy
    - type: f1
      value: 0.630
      name: F1
---

# 💕 Relationship Longevity Predictor — v2.0

**An ensemble ML model that predicts long-term relationship compatibility from two people's profiles, grounded in Gottman's Four Horsemen and Cox proportional hazards survival analysis.**

👉 **[Try the live demo →](https://huggingface.co/spaces/Builder-Neekhil/relationship-longevity-predictor-demo)**

---

## What this is (and isn't)

**Is:** A well-calibrated research artifact. An ensemble (XGBoost + LightGBM + CatBoost) trained on three open datasets, with Gottman behavioral proxies and survival priors layered in. Think of it as a **mirror** that reflects patterns the literature has documented — not a crystal ball.

**Isn't:** A decision tool. Don't break up, propose, or pick a partner based on its output. The interesting question isn't "what score did I get" — it's "which of the Four Horsemen showed up in my top factors, and why."

**Training data is narrow:** Columbia speed-daters (2002–2004), 170 Turkish couples from the Yöntem Gottman study, and 14,688 public-figure marriages pulled from a dataset originally compiled by Vedastro for unrelated research (we used only the marriage/divorce metadata — no astrological features). Generalization beyond these cohorts is unverified. See Limitations.

---

## 📊 Headline Results

| Metric | v1.0 Baseline | v2.0 Enhanced | Change |
| --- | --- | --- | --- |
| **AUC-ROC** | 0.8842 | **0.8896** | +0.0055 ✅ |
| **AUC-PR** | 0.6933 | **0.7108** | +0.0175 ✅ |
| **Brier Score** | 0.0960 | **0.0934** | -0.0026 ✅ |
| **Accuracy** | 83.5% | **85.9%** | +2.4% ✅ |
| **F1 Score** | 0.620 | **0.630** | +0.010 ✅ |
| **Precision** | 52.4% | **58.9%** | +6.5% ✅ |

**Key improvement: +12.3% precision boost** — far fewer false positives than v1.

### What Changed

v2.0 adds **20 new features** from two additional data sources:

| Source | Features Added | Signal |
|--------|:-:|---|
| **Gottman Behavioral Model** (Phase 1) | 13 | Contempt, criticism, defensiveness, stonewalling proxy scores derived from 170-couple divorce study |
| **Marriage Duration Survival Model** (Phase 2) | 7 | Longevity priors from 14,688 real marriages (age-risk, relationship-history risk, timing hazard) |

**8 of the 20 new features ranked in the top 30** most important features by SHAP:

| Rank | New Feature | SHAP | Source |
|:---:|---|:---:|---|
| 3 | `gottman_proxy_love_maps` | 0.447 | 🔴 Gottman |
| 4 | `gottman_proxy_contempt_x_stonewalling` | 0.403 | 🔴 Gottman |
| 8 | `gottman_proxy_ratio` | 0.306 | 🔴 Gottman |
| 10 | `gottman_proxy_stonewalling` | 0.279 | 🔴 Gottman |
| 12 | `gottman_proxy_horsemen` | 0.264 | 🔴 Gottman |
| 21 | `gottman_proxy_net_risk` | 0.189 | 🔴 Gottman |
| 27 | `survival_age_gap_risk` | 0.163 | 🔵 Survival |
| 29 | `gottman_proxy_contempt` | 0.160 | 🔴 Gottman |

---

## Phase 1: Gottman Behavioral Model

**Dataset:** Yöntem et al. Divorce Predictors — 170 married/divorced Turkish couples, 54 Gottman-mapped behavioral questions.

**Standalone performance:** AUC = **0.998**, Accuracy = **98.2%** on predicting divorce from behavioral patterns.

The 54 questions map to Gottman's relationship theory:

| Gottman Dimension | Questions | What It Measures |
|---|:-:|---|
| **Shared Goals** | Q1-Q10 | Aligned life direction, quality time, common objectives |
| **Love Maps** | Q11-Q20 | Values alignment, role expectations, compatibility beliefs |
| **Love Maps Deep** | Q21-Q30 | Knowing partner's inner world, stress, hopes, anxieties |
| **Criticism** | Q31-Q32, Q37-Q38 | Attacking character, negative statements, sudden arguments |
| **Contempt** | Q33-Q36, Q39-Q40 | Insults, humiliation, anger escalation, hatred |
| **Defensiveness** | Q41, Q45-Q46, Q48-Q50 | Blame-shifting, victimhood, refusing responsibility |
| **Stonewalling** | Q42-Q44, Q47 | Silence, withdrawal, leaving, shutting down |
| **Deep Contempt** | Q51-Q54 | Attributing meanness, vindictiveness, pathology to partner |

**Top divorce predictor by SHAP:** `love_maps × shared_goals` interaction — couples who *both* lack shared goals *and* don't know each other's inner world face the highest divorce risk.

### Gottman Proxy Features (mapped to speed dating data)

Since speed dating participants didn't answer the 54 Gottman questions, we created **proxy scores** by mapping their existing personality/perception data to Gottman dimensions:

| Proxy | Derived From |
|---|---|
| `gottman_proxy_contempt` | Low mutual scores + high perception gaps |
| `gottman_proxy_criticism` | Misaligned values + asymmetric ratings |
| `gottman_proxy_defensiveness` | Self-rating inflation vs partner perception |
| `gottman_proxy_stonewalling` | Low engagement, low liking, no shared interests |
| `gottman_proxy_love_maps` | Interest correlation + shared interests + mutual perception accuracy |
| `gottman_proxy_shared_goals` | Value alignment + interest overlap |
| `gottman_proxy_ratio` | The famous Gottman 5:1 positive-to-negative ratio |

---

## Phase 2: Marriage Duration Survival Model

**Dataset:** [vedastro-org/15000-Famous-People-Marriage-Divorce-Info](https://hf.co/datasets/vedastro-org/15000-Famous-People-Marriage-Divorce-Info) — 14,688 marriage records from 12,353 famous people.

### Key Findings

| Finding | Statistic |
|---|---|
| **Overall divorce rate** | 34.5% |
| **Median divorce timing** | 7 years |
| **Most dangerous period** | 3-7 years (41.1% of all divorces) |
| **Love marriage divorce rate** | 34.1% |
| **Arranged marriage divorce rate** | 23.4% (p=0.006, significantly lower) |
| **First marriage divorce rate** | 27.8% |
| **Subsequent marriage divorce rate** | **69.3%** |

### Cox Proportional Hazards Model (Concordance = 0.64)

| Factor | Hazard Ratio | p-value | Meaning |
|---|:---:|:---:|---|
| **Is first marriage** | **0.26** | <0.001 | 74% lower divorce hazard than subsequent marriages |
| **Is love marriage** | **0.77** | 0.002 | 23% lower hazard than non-love marriages |
| **Age at marriage** | **0.96** | <0.001 | Each year older → 4% lower divorce hazard |
| **Marriage number** | **1.34** | <0.001 | Each additional marriage → 34% higher hazard |

### Divorce Timing Distribution

![Divorce Timing](phase2_survival_model/figures/divorce_timing.png)

### Kaplan-Meier Survival Curves

![KM by Type](phase2_survival_model/figures/km_by_type.png)
![KM by Marriage Number](phase2_survival_model/figures/km_by_marriage_number.png)

---

## Model Architecture (v2.0)

**Ensemble of 3 gradient-boosted tree models** with **133 engineered features** (113 original + 13 Gottman + 7 survival):

| Model | Weight | v1 AUC | v2 AUC | Change |
|-------|:---:|:---:|:---:|:---:|
| XGBoost | 0.40 | 0.8852 | 0.8920 | +0.0068 |
| LightGBM | 0.35 | 0.8912 | **0.9011** | +0.0099 |
| CatBoost | 0.25 | 0.8661 | 0.8688 | +0.0027 |
| **Ensemble** | — | 0.8842 | **0.8896** | +0.0055 |

## Visualizations

### v1 vs v2 ROC Comparison
![ROC Comparison](v2_enhanced/figures/roc_comparison.png)

### Metrics Comparison
![Metrics Comparison](v2_enhanced/figures/metrics_comparison.png)

### Feature Source Contribution
![Source Contribution](v2_enhanced/figures/source_contribution.png)

### Enhanced SHAP Summary (v2)
![Enhanced SHAP](v2_enhanced/figures/enhanced_shap_summary.png)

### v1 Visualizations
| | |
|---|---|
| ![ROC Curves](figures/roc_curves.png) | ![SHAP Summary](figures/shap_summary.png) |
| ![Feature Importance](figures/feature_importance.png) | ![Confusion Matrix](figures/confusion_matrix.png) |

---

## Training Data

| Dataset | Records | Role |
|---|:---:|---|
| [mstz/speeddating](https://hf.co/datasets/mstz/speeddating) | 1,048 encounters | Primary training data — individual profiles + match outcome |
| Yöntem et al. Divorce Predictors (Kaggle) | 170 couples | Phase 1 — Gottman behavioral feature engineering |
| [vedastro-org/15000-Famous-People-Marriage-Divorce-Info](https://hf.co/datasets/vedastro-org/15000-Famous-People-Marriage-Divorce-Info) | 14,688 marriages | Phase 2 — Longevity priors + survival analysis |

## Literature Basis

| Paper | Contribution |
|-------|-------------|
| Grinsztajn et al. (NeurIPS 2022) — *"Why do tree-based models still outperform deep learning on tabular data?"* | Validated XGBoost/LightGBM as SOTA for medium-sized tabular data |
| Fisman et al. (QJE 2006) — *"Gender Differences in Mate Selection"* | Original speed dating experiment; ~70% accuracy with logistic regression |
| **Gottman & Silver (1999) — *"The Seven Principles for Making Marriage Work"*** | **Four Horsemen framework: contempt, criticism, defensiveness, stonewalling** |
| **Yöntem et al. (2019) — *"Divorce Prediction Using Correlation Based Feature Selection"*** | **54-question Gottman-mapped divorce predictor; published 97.7% accuracy** |
| Savcisens et al. (Nature Human Behaviour 2024) — *"Using Sequences of Life-events to Predict Human Lives"* | life2vec — longitudinal prediction architecture |

## Repo Structure

```
├── # v1.0 Baseline Model
├── xgboost_model.joblib, lightgbm_model.joblib, catboost_model.cbm
├── ensemble_config.json, feature_columns.joblib
├── figures/                          # v1 plots
│
├── # Phase 1 — Gottman Behavioral Model
├── phase1_divorce_model/
│   ├── divorce_xgb.joblib, divorce_lgb.joblib, divorce_cat.cbm
│   ├── gottman_recipe.json           # Dimension mappings + importance
│   ├── gottman_mapping.joblib
│   └── figures/                      # SHAP, confusion matrix, dimension importance
│
├── # Phase 2 — Survival Model
├── phase2_survival_model/
│   ├── longevity_priors.json         # Base rates by type/era/age/marriage#
│   ├── survival_recipe.json          # Cox PH + KM + timing distributions
│   └── figures/                      # KM curves, Cox hazard ratios, timing
│
├── # v2.0 Enhanced Model (RECOMMENDED)
├── v2_enhanced/
│   ├── enhanced_xgb.joblib, enhanced_lgb.joblib, enhanced_cat.cbm
│   ├── enhanced_config.json          # Weights, features, metrics, improvements
│   ├── enhanced_feature_columns.joblib
│   └── figures/                      # Comparison plots, SHAP
│
└── # Training Scripts (fully reproducible)
    ├── train_relationship_predictor.py   # v1 baseline
    ├── phase1_divorce_model.py           # Gottman behavioral model
    ├── phase2_marriage_duration.py       # Survival analysis
    └── phase3_integration.py             # Integration + comparison
```

## Limitations & Ethics

**Cohort bias.** The primary training signal is from Columbia University speed-daters in 2002–2004. This is a narrow demographic slice — predominantly educated, urban, US-based, early-internet-era. Generalization to other populations is unverified and should be assumed weak until tested.

**Celebrity bias in the survival priors.** The 14,688-marriage Vedastro dataset is public-figure-heavy, with known elevated divorce rates and atypical relationship dynamics (media exposure, wealth asymmetry, career mobility). The arranged-vs-love finding (23.4% vs 34.1%) is descriptive of this dataset, not a general claim about relationship types.

**Dataset provenance.** The Vedastro dataset was originally compiled for astrology research. This model uses only the structured marriage/divorce metadata (age at marriage, marriage number, duration, type, outcome) — no astrological variables are used as features.

**Short-horizon proxy.** Speed-dating captures initial match decisions, not long-term outcomes. The Gottman and survival layers partially bridge this gap, but they're proxies, not ground truth.

**Small Gottman sample.** The underlying divorce predictor was trained on 170 couples. The Four Horsemen framework itself is robust across decades of research; the proxy mapping from speed-dating features to Gottman dimensions is approximate and worth questioning.

**Not a decision tool.** Outputs are probabilistic, directional, and should be treated as a conversation starter — not advice. This model should not be used to make real decisions about real relationships.

## License

cc-by-nc-4.0 Research use. Based on publicly available academic datasets.

---

*Built with XGBoost, LightGBM, CatBoost, SHAP, lifelines, and scikit-learn.*