![\"Dead_biomass_analysis\"](\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F11351811%2F81a70fd208bf2fae771ae19deb98dc46%2Fdead_biomass_analysis.png?generation=1764657327476896&alt=media\")
In this discussion, the competition host mentioned:
\n\n\n\"Some dead matter hidden below may be invisible from the surface, which could make it difficult for the prediction to align with the ground truth. Perhaps we could consider using the metadata Height_Ave_cm? (This is just my personal opinion and has not been validated.)\"
\n
Many of us have noticed that Dry_Dead_g is the hardest target to predict. I decided to validate this hypothesis with a comprehensive statistical analysis of the training data.
\n❌ Height_Ave_cm alone is NOT a strong predictor of Dead biomass
\n✅ What DOES predict Dead biomass:
\n✅ Why Dead is so hard:
\nI tested the relationship between Height_Ave_cm and Dry_Dead_g using multiple methods:
| Metric | \nValue | \nInterpretation | \n
|---|---|---|
| Pearson Correlation | \n-0.0497 | \nVery weak negative (almost no linear relationship) | \n
| Spearman Correlation | \n+0.2109 | \nWeak positive (weak monotonic relationship) | \n
| Linear Regression R² | \n0.0025 | \nHeight explains only 0.25% of Dead variance | \n
| T-test (High vs Low Dead) | \np=0.766 | \nNo significant height difference between groups | \n
Conclusion: Height_Ave_cm alone does not significantly predict Dead biomass.
\nHere are the top 5 predictors by Pearson correlation:
\n| Feature | \nPearson r | \nSpearman r | \nType | \n
|---|---|---|---|
| Dead_per_cm_Height | \n+0.7424 | \n+0.8631 | \nDerived (Dead/Height ratio) | \n
| Dead_to_Total_Ratio | \n+0.7088 | \n+0.8375 | \nDerived (Dead/Total ratio) | \n
| Dead_to_GDM_Ratio | \n+0.5316 | \n+0.8379 | \nDerived (Dead/GDM ratio) | \n
| Dry_Total_g | \n+0.4539 | \n+0.3886 | \nDirect target | \n
| Dry_Clover_g | \n-0.1755 | \n-0.2092 | \nDirect target (negative) | \n
Key Insight: Dead biomass is best understood as a proportion/ratio rather than absolute value. The derived ratio features have much stronger correlations (0.71-0.74) than any raw metadata features (0.05-0.21).
\nI calculated the Coefficient of Variation (CV = std/mean) for all targets:
\n: . (hardest - extreme variance)\n: . (nd hardest) ⚠️\n: .\n: .\n: . (easiest - most stable)\nInterpretation: Dead has a CV of 1.03, meaning the standard deviation exceeds the mean! This extreme variability makes accurate prediction very difficult.
\nDead biomass varies dramatically by state:
\n| State | \nCount | \nMean Dead (g) | \nStd Dev | \nMin | \nMax | \n
|---|---|---|---|---|---|
| WA | \n32 | \n0.00 | \n0.00 | \n0.0 | \n0.0 | \n
| Tas | \n138 | \n15.23 | \n12.43 | \n0.4 | \n58.7 | \n
| NSW | \n75 | \n14.20 | \n15.63 | \n0.0 | \n83.8 | \n
| Vic | \n112 | \n10.11 | \n8.86 | \n0.0 | \n38.9 | \n
Key Finding: All 32 Western Australia samples have Dead = 0. This is a guaranteed improvement opportunity - any WA prediction should be forced to 0.
\nBy Season:
\nBy Dominant Species:
\nThe host's hypothesis was that height might capture \"hidden\" dead matter below the surface. Here's what the data shows:
\nDead by Height Bin:
\n Bin Count Mean Dead (g) Std Dev\n Low . .\n . .\n . .\n . .\n High . .\nObservation: There's actually a negative trend - taller pastures tend to have less dead matter on average. This is counterintuitive to the hypothesis.
\nLinear Regression: Dry_Dead_g = -0.06 * Height_Ave_cm + 12.5
Based on this analysis, here are my recommendations:
\n \n total_pred = model_head_total(features)\n dead_ratio = sigmoid(model_head_ratio(features)) \n dead_pred = total_pred * dead_ratio\n loss_dead = F.smooth_l1_loss(pred_dead, true_dead, beta=)\n state == :\n dead_pred = \nEnsemble strategy
\nAdd auxiliary task: Dead/Total ratio
\nDon't use Height_Ave_cm as direct input for Dead prediction
\nDon't treat all states equally
\nI created a 9-panel analysis plot showing:
\n
The competition host's hypothesis about Height_Ave_cm was well-intentioned, but the data doesn't support it as a strong predictor. However, this analysis revealed something more useful:
\nDead biomass should be modeled as a proportion/ratio of total biomass, not as an absolute value.
\nThe strongest signal comes from:
\nThis architectural change could potentially improve Dead predictions significantly.
\nI've created a comprehensive Python script that performs this entire analysis. It includes:
\nIf anyone wants the code, let me know and I can share it!
\nWhat are your experiences with Dead biomass prediction? Have you found any other patterns or strategies that work?
\nI'm curious if anyone has tried ratio-based architectures or state-specific modeling. Would love to hear your thoughts! 🌱
", "messages": [ { "id": "3360414", "postDate": "12/02/2025 06:37:09", "content": "In this discussion, the competition host mentioned:
\n\n\n\"Some dead matter hidden below may be invisible from the surface, which could make it difficult for the prediction to align with the ground truth. Perhaps we could consider using the metadata Height_Ave_cm? (This is just my personal opinion and has not been validated.)\"
\n
Many of us have noticed that Dry_Dead_g is the hardest target to predict. I decided to validate this hypothesis with a comprehensive statistical analysis of the training data.
\n❌ Height_Ave_cm alone is NOT a strong predictor of Dead biomass
\n✅ What DOES predict Dead biomass:
\n✅ Why Dead is so hard:
\nI tested the relationship between Height_Ave_cm and Dry_Dead_g using multiple methods:
| Metric | \nValue | \nInterpretation | \n
|---|---|---|
| Pearson Correlation | \n-0.0497 | \nVery weak negative (almost no linear relationship) | \n
| Spearman Correlation | \n+0.2109 | \nWeak positive (weak monotonic relationship) | \n
| Linear Regression R² | \n0.0025 | \nHeight explains only 0.25% of Dead variance | \n
| T-test (High vs Low Dead) | \np=0.766 | \nNo significant height difference between groups | \n
Conclusion: Height_Ave_cm alone does not significantly predict Dead biomass.
\nHere are the top 5 predictors by Pearson correlation:
\n| Feature | \nPearson r | \nSpearman r | \nType | \n
|---|---|---|---|
| Dead_per_cm_Height | \n+0.7424 | \n+0.8631 | \nDerived (Dead/Height ratio) | \n
| Dead_to_Total_Ratio | \n+0.7088 | \n+0.8375 | \nDerived (Dead/Total ratio) | \n
| Dead_to_GDM_Ratio | \n+0.5316 | \n+0.8379 | \nDerived (Dead/GDM ratio) | \n
| Dry_Total_g | \n+0.4539 | \n+0.3886 | \nDirect target | \n
| Dry_Clover_g | \n-0.1755 | \n-0.2092 | \nDirect target (negative) | \n
Key Insight: Dead biomass is best understood as a proportion/ratio rather than absolute value. The derived ratio features have much stronger correlations (0.71-0.74) than any raw metadata features (0.05-0.21).
\nI calculated the Coefficient of Variation (CV = std/mean) for all targets:
\n: . (hardest - extreme variance)\n: . (nd hardest) ⚠️\n: .\n: .\n: . (easiest - most stable)\nInterpretation: Dead has a CV of 1.03, meaning the standard deviation exceeds the mean! This extreme variability makes accurate prediction very difficult.
\nDead biomass varies dramatically by state:
\n| State | \nCount | \nMean Dead (g) | \nStd Dev | \nMin | \nMax | \n
|---|---|---|---|---|---|
| WA | \n32 | \n0.00 | \n0.00 | \n0.0 | \n0.0 | \n
| Tas | \n138 | \n15.23 | \n12.43 | \n0.4 | \n58.7 | \n
| NSW | \n75 | \n14.20 | \n15.63 | \n0.0 | \n83.8 | \n
| Vic | \n112 | \n10.11 | \n8.86 | \n0.0 | \n38.9 | \n
Key Finding: All 32 Western Australia samples have Dead = 0. This is a guaranteed improvement opportunity - any WA prediction should be forced to 0.
\nBy Season:
\nBy Dominant Species:
\nThe host's hypothesis was that height might capture \"hidden\" dead matter below the surface. Here's what the data shows:
\nDead by Height Bin:
\n Bin Count Mean Dead (g) Std Dev\n Low . .\n . .\n . .\n . .\n High . .\nObservation: There's actually a negative trend - taller pastures tend to have less dead matter on average. This is counterintuitive to the hypothesis.
\nLinear Regression: Dry_Dead_g = -0.06 * Height_Ave_cm + 12.5
Based on this analysis, here are my recommendations:
\n \n total_pred = model_head_total(features)\n dead_ratio = sigmoid(model_head_ratio(features)) \n dead_pred = total_pred * dead_ratio\n loss_dead = F.smooth_l1_loss(pred_dead, true_dead, beta=)\n state == :\n dead_pred = \nEnsemble strategy
\nAdd auxiliary task: Dead/Total ratio
\nDon't use Height_Ave_cm as direct input for Dead prediction
\nDon't treat all states equally
\nI created a 9-panel analysis plot showing:
\n
The competition host's hypothesis about Height_Ave_cm was well-intentioned, but the data doesn't support it as a strong predictor. However, this analysis revealed something more useful:
\nDead biomass should be modeled as a proportion/ratio of total biomass, not as an absolute value.
\nThe strongest signal comes from:
\nThis architectural change could potentially improve Dead predictions significantly.
\nI've created a comprehensive Python script that performs this entire analysis. It includes:
\nIf anyone wants the code, let me know and I can share it!
\nWhat are your experiences with Dead biomass prediction? Have you found any other patterns or strategies that work?
\nI'm curious if anyone has tried ratio-based architectures or state-specific modeling. Would love to hear your thoughts! 🌱
", "rawMarkdown": "# Analysis: Height_Ave_cm and Dead Biomass Prediction - Testing the Host's Hypothesis\n\n## Background\n\nIn [this discussion](https://www.kaggle.com/competitions/csiro-biomass/discussion/615516), the competition host mentioned:\n\n> \"Some dead matter hidden below may be invisible from the surface, which could make it difficult for the prediction to align with the ground truth. Perhaps we could consider using the metadata Height_Ave_cm? (This is just my personal opinion and has not been validated.)\"\n\nMany of us have noticed that **Dry_Dead_g is the hardest target to predict**. I decided to validate this hypothesis with a comprehensive statistical analysis of the training data.\n\n---\n\n## TL;DR - Key Findings\n\n❌ **Height_Ave_cm alone is NOT a strong predictor of Dead biomass**\n- Pearson correlation: **-0.0497** (essentially zero)\n- Spearman correlation: **+0.2109** (weak)\n- R² score: **0.0025** (explains only 0.25% of variance)\n\n✅ **What DOES predict Dead biomass:**\n- **Dead/Total ratio** (r=0.71) - Dead as proportion of total biomass\n- **Dead/GDM ratio** (r=0.53) - Dead relative to green matter\n- **Total biomass** (r=0.45) - Absolute total biomass\n\n✅ **Why Dead is so hard:**\n- **Coefficient of Variation = 1.03** (2nd highest after Clover at 1.82)\n- Standard deviation > mean = extreme variability\n- State-specific patterns (WA has ALL zeros, NSW has highest variance)\n\n---\n\n## Detailed Analysis\n\n### 1. Correlation Analysis: Height vs Dead\n\nI tested the relationship between `Height_Ave_cm` and `Dry_Dead_g` using multiple methods:\n\n| Metric | Value | Interpretation |\n|--------|-------|----------------|\n| **Pearson Correlation** | -0.0497 | Very weak negative (almost no linear relationship) |\n| **Spearman Correlation** | +0.2109 | Weak positive (weak monotonic relationship) |\n| **Linear Regression R²** | 0.0025 | Height explains only **0.25%** of Dead variance |\n| **T-test (High vs Low Dead)** | p=0.766 | No significant height difference between groups |\n\n**Conclusion**: Height_Ave_cm alone **does not** significantly predict Dead biomass.\n\n---\n\n### 2. What Actually Correlates with Dead?\n\nHere are the **top 5 predictors** by Pearson correlation:\n\n| Feature | Pearson r | Spearman r | Type |\n|---------|-----------|------------|------|\n| **Dead_per_cm_Height** | +0.7424 | +0.8631 | Derived (Dead/Height ratio) |\n| **Dead_to_Total_Ratio** | +0.7088 | +0.8375 | Derived (Dead/Total ratio) |\n| **Dead_to_GDM_Ratio** | +0.5316 | +0.8379 | Derived (Dead/GDM ratio) |\n| **Dry_Total_g** | +0.4539 | +0.3886 | Direct target |\n| **Dry_Clover_g** | -0.1755 | -0.2092 | Direct target (negative) |\n\n**Key Insight**: Dead biomass is best understood as a **proportion/ratio** rather than absolute value. The derived ratio features have **much stronger correlations** (0.71-0.74) than any raw metadata features (0.05-0.21).\n\n---\n\n### 3. Why is Dead the Hardest Target?\n\nI calculated the **Coefficient of Variation (CV = std/mean)** for all targets:\n\n```\nDry_Clover_g: 1.8223 (hardest - extreme variance)\nDry_Dead_g: 1.0297 (2nd hardest) ⚠️\nDry_Green_g: 0.9540\nGDM_g: 0.7494\nDry_Total_g: 0.6175 (easiest - most stable)\n```\n\n**Interpretation**: Dead has a CV of 1.03, meaning the standard deviation exceeds the mean! This extreme variability makes accurate prediction very difficult.\n\n---\n\n### 4. State-Specific Patterns\n\nDead biomass varies dramatically by state:\n\n| State | Count | Mean Dead (g) | Std Dev | Min | Max |\n|-------|-------|---------------|---------|-----|-----|\n| **WA** | 32 | **0.00** | 0.00 | 0.0 | 0.0 |\n| **Tas** | 138 | 15.23 | 12.43 | 0.4 | 58.7 |\n| **NSW** | 75 | 14.20 | **15.63** | 0.0 | 83.8 |\n| **Vic** | 112 | 10.11 | 8.86 | 0.0 | 38.9 |\n\n**Key Finding**: All 32 Western Australia samples have **Dead = 0**. This is a guaranteed improvement opportunity - any WA prediction should be forced to 0.\n\n---\n\n### 5. Seasonal and Species Patterns\n\n**By Season:**\n- **Spring**: Highest Dead (15.2g) - dormancy period\n- **Winter**: Lowest Dead (9.2g) - new growth\n- **Autumn**: Highest variance (std=15.3g) - transition\n\n**By Dominant Species:**\n- **Fescue**: Highest Dead (22.7g) - accumulates dry matter\n- **Clover/Lucerne**: Lowest Dead (~4.8g) - green-dominant\n- **Ryegrass**: Moderate Dead (12.9g)\n\n---\n\n### 6. Height Analysis Deep Dive\n\nThe host's hypothesis was that height might capture \"hidden\" dead matter below the surface. Here's what the data shows:\n\n**Dead by Height Bin:**\n```\nHeight Bin Count Mean Dead (g) Std Dev\nVery Low 317 12.62 12.30\nLow 16 11.80 14.07\nMedium 16 4.98 12.94\nHigh 5 3.45 2.22\nVery High 3 4.70 2.25\n```\n\n**Observation**: There's actually a **negative trend** - taller pastures tend to have *less* dead matter on average. This is counterintuitive to the hypothesis.\n\n**Linear Regression**: `Dry_Dead_g = -0.06 * Height_Ave_cm + 12.5`\n- Coefficient: -0.06 (slightly negative)\n- R² = 0.0025 (explains almost nothing)\n\n---\n\n## Recommendations for Modeling\n\nBased on this analysis, here are my recommendations:\n\n### ✅ DO This\n\n1. **Predict Dead as a ratio, not absolute value**\n ```python\n # Predict Total first, then Dead/Total ratio\n total_pred = model_head_total(features)\n dead_ratio = sigmoid(model_head_ratio(features)) # Bounded 0-1\n dead_pred = total_pred * dead_ratio\n ```\n\n2. **Use Huber Loss for Dead predictions**\n - Dead has extreme outliers (0 to 84g)\n - Huber loss is more robust than MSE\n ```python\n loss_dead = F.smooth_l1_loss(pred_dead, true_dead, beta=5.0)\n ```\n\n3. **Add state-specific handling**\n ```python\n if state == 'WA':\n dead_pred = 0.0 # All WA samples are zero\n ```\n\n4. **Ensemble strategy**\n - Dead has highest variance → benefits most from ensemble\n - Consider training separate model on high-Dead samples (>20g)\n\n5. **Add auxiliary task: Dead/Total ratio**\n - This ratio has 0.71 correlation vs raw Dead at 0.05\n - Multi-task learning could help\n\n### ❌ DON'T Do This\n\n1. **Don't use Height_Ave_cm as direct input for Dead prediction**\n - R²=0.0025 - almost no predictive power\n - Better to use it in *derived features* (Dead_per_cm_Height)\n\n2. **Don't treat all states equally**\n - WA is fundamentally different (always zero)\n - Consider state embeddings or separate heads\n\n---\n\n## Visualization\n\nI created a 9-panel analysis plot showing:\n- Height vs Dead scatter (weak relationship)\n- NDVI vs Dead scatter\n- Total vs Dead scatter\n- Correlation heatmap\n- State/Season distributions\n- Dead histogram and ratios\n\n\n\n---\n\n## Conclusion\n\nThe competition host's hypothesis about Height_Ave_cm was well-intentioned, but **the data doesn't support it as a strong predictor**. However, this analysis revealed something more useful:\n\n**Dead biomass should be modeled as a proportion/ratio of total biomass, not as an absolute value.**\n\nThe strongest signal comes from:\n1. Predicting Total biomass accurately first\n2. Then predicting Dead/Total ratio (0.71 correlation)\n3. Multiplying to get Dead_g\n\nThis architectural change could potentially improve Dead predictions significantly.\n\n---\n\n## Analysis Code\n\nI've created a comprehensive Python script that performs this entire analysis. It includes:\n- Correlation analysis (Pearson/Spearman)\n- Categorical analysis (State/Season/Species)\n- Extreme case analysis\n- Statistical tests (t-tests, regression)\n- Visualization (9-panel plot)\n\nIf anyone wants the code, let me know and I can share it!\n\n---\n\n**What are your experiences with Dead biomass prediction? Have you found any other patterns or strategies that work?**\n\nI'm curious if anyone has tried ratio-based architectures or state-specific modeling. Would love to hear your thoughts! 🌱", "votes": null }, { "id": "3360421", "postDate": "12/02/2025 06:49:18", "content": "Great find!
", "rawMarkdown": "Great find!", "votes": null }, { "id": "3360651", "postDate": "12/02/2025 12:19:14", "content": "I have a gut feeling that we should still find the internal connections between these 5 predicted values. But I can't really put my finger on what kind of connections they are
", "rawMarkdown": "I have a gut feeling that we should still find the internal connections between these 5 predicted values. But I can't really put my finger on what kind of connections they are", "votes": null }, { "id": "3361657", "postDate": "12/03/2025 17:49:31", "content": "can we do some sort of feature engeneering or merging different models trained on different data?
", "rawMarkdown": "can we do some sort of feature engeneering or merging different models trained on different data?", "votes": null }, { "id": "3362798", "postDate": "12/05/2025 10:33:31", "content": "Great discovery! Thanks a lot for verifying that!
", "rawMarkdown": "Great discovery! Thanks a lot for verifying that!", "votes": null }, { "id": "3362877", "postDate": "12/05/2025 13:50:25", "content": " Add state-specific handling\n\n state == :\n dead_pred = \nIn test set there is no cols contain state or there information then how we apply this ?
", "rawMarkdown": "```python\n\n Add state-specific handling\n\n if state == 'WA':\n dead_pred = 0.0 # All WA samples are zero\n\n```\n\nIn test set there is no cols contain state or there information then how we apply this ?", "votes": null }, { "id": "3362928", "postDate": "12/05/2025 15:41:05", "content": "NSP: Next Sentence Prediction inherently make a language model learn Topic Classification.\nSame way, Learning Dead inherently makes our models good state classifiers, whether you make your model learn it as an additional task or not.
\nStill if you wish, you can use a separate model for that or an auxiliary task.
", "rawMarkdown": "NSP: Next Sentence Prediction inherently make a language model learn Topic Classification.\nSame way, Learning Dead inherently makes our models good state classifiers, whether you make your model learn it as an additional task or not.\n\nStill if you wish, you can use a separate model for that or an auxiliary task.", "votes": null }, { "id": "3368445", "postDate": "12/09/2025 08:27:53", "content": "Thanks for sharing your knowledge on statistics, that is essential for Kaggle competition.
", "rawMarkdown": "Thanks for sharing your knowledge on statistics, that is essential for Kaggle competition.", "votes": null }, { "id": "3370172", "postDate": "12/10/2025 16:00:04", "content": "In my case 'Height_Ave_cm' slightly help to get 0.58 on LB.
", "rawMarkdown": "In my case 'Height_Ave_cm' slightly help to get 0.58 on LB.", "votes": null }, { "id": "3381482", "postDate": "12/24/2025 17:53:29", "content": "This is a great analysis. I tried using state as a multi-task/auxiliary learning task but I can only get a boost on my val set. It doesn't seem to translate to the leaderboard.
\nThe ratio-based architecture is quite interesting though!
", "rawMarkdown": "This is a great analysis. I tried using state as a multi-task/auxiliary learning task but I can only get a boost on my val set. It doesn't seem to translate to the leaderboard.\n\nThe ratio-based architecture is quite interesting though!", "votes": null }, { "id": "3381841", "postDate": "12/25/2025 17:27:00", "content": "I had a small doubt, please help me, In test.csv we just have sample_id, image_path, target_name then how can we get these values ?
", "rawMarkdown": "I had a small doubt, please help me, In test.csv we just have sample_id, image_path, target_name then how can we get these values ?", "votes": null }, { "id": "3381940", "postDate": "12/26/2025 02:11:33", "content": "The only way to get them is to build a model that predicts them - if we had a very large set of images for our train data set I would think the approach would not work - after all any model looking at the images might find those relationships on its own. But with only 357 images it has a chance of providing benefit. I have it on my TODO list, but worried that I am running out of time as I struggle to get above 0.68.
", "rawMarkdown": "The only way to get them is to build a model that predicts them - if we had a very large set of images for our train data set I would think the approach would not work - after all any model looking at the images might find those relationships on its own. But with only 357 images it has a chance of providing benefit. I have it on my TODO list, but worried that I am running out of time as I struggle to get above 0.68.", "votes": null } ], "comments": [ { "id": 3360421, "author_name": "paritoshtripathi5", "author_url": "", "post_date": "12/02/2025 06:49:18", "content": "Great find!
", "votes": null, "replies": [] }, { "id": 3360651, "author_name": "", "author_url": "", "post_date": "12/02/2025 12:19:14", "content": "I have a gut feeling that we should still find the internal connections between these 5 predicted values. But I can't really put my finger on what kind of connections they are
", "votes": null, "replies": [] }, { "id": 3361657, "author_name": "craftycode", "author_url": "", "post_date": "12/03/2025 17:49:31", "content": "can we do some sort of feature engeneering or merging different models trained on different data?
", "votes": null, "replies": [] }, { "id": 3362798, "author_name": "qiyuliaocsiro", "author_url": "", "post_date": "12/05/2025 10:33:31", "content": "Great discovery! Thanks a lot for verifying that!
", "votes": null, "replies": [] }, { "id": 3362877, "author_name": "sangrampatil5150", "author_url": "", "post_date": "12/05/2025 13:50:25", "content": " Add state-specific handling\n\n state == :\n dead_pred = \nIn test set there is no cols contain state or there information then how we apply this ?
", "votes": null, "replies": [ { "id": 3362928, "author_name": "komilparmar", "author_url": "", "post_date": "12/05/2025 15:41:05", "content": "NSP: Next Sentence Prediction inherently make a language model learn Topic Classification.\nSame way, Learning Dead inherently makes our models good state classifiers, whether you make your model learn it as an additional task or not.
\nStill if you wish, you can use a separate model for that or an auxiliary task.
", "votes": null, "replies": [] } ] }, { "id": 3368445, "author_name": "kebenny", "author_url": "", "post_date": "12/09/2025 08:27:53", "content": "Thanks for sharing your knowledge on statistics, that is essential for Kaggle competition.
", "votes": null, "replies": [] }, { "id": 3370172, "author_name": "rustambazarbayev", "author_url": "", "post_date": "12/10/2025 16:00:04", "content": "In my case 'Height_Ave_cm' slightly help to get 0.58 on LB.
", "votes": null, "replies": [] }, { "id": 3381482, "author_name": "zahid1", "author_url": "", "post_date": "12/24/2025 17:53:29", "content": "This is a great analysis. I tried using state as a multi-task/auxiliary learning task but I can only get a boost on my val set. It doesn't seem to translate to the leaderboard.
\nThe ratio-based architecture is quite interesting though!
", "votes": null, "replies": [] }, { "id": 3381841, "author_name": "rajatshedshyal", "author_url": "", "post_date": "12/25/2025 17:27:00", "content": "I had a small doubt, please help me, In test.csv we just have sample_id, image_path, target_name then how can we get these values ?
", "votes": null, "replies": [ { "id": 3381940, "author_name": "pcjimmmy", "author_url": "", "post_date": "12/26/2025 02:11:33", "content": "The only way to get them is to build a model that predicts them - if we had a very large set of images for our train data set I would think the approach would not work - after all any model looking at the images might find those relationships on its own. But with only 357 images it has a chance of providing benefit. I have it on my TODO list, but worried that I am running out of time as I struggle to get above 0.68.
", "votes": null, "replies": [] } ] } ], "raw_markdown_by_id": { "3360414": "# Analysis: Height_Ave_cm and Dead Biomass Prediction - Testing the Host's Hypothesis\n\n## Background\n\nIn [this discussion](https://www.kaggle.com/competitions/csiro-biomass/discussion/615516), the competition host mentioned:\n\n> \"Some dead matter hidden below may be invisible from the surface, which could make it difficult for the prediction to align with the ground truth. Perhaps we could consider using the metadata Height_Ave_cm? (This is just my personal opinion and has not been validated.)\"\n\nMany of us have noticed that **Dry_Dead_g is the hardest target to predict**. I decided to validate this hypothesis with a comprehensive statistical analysis of the training data.\n\n---\n\n## TL;DR - Key Findings\n\n❌ **Height_Ave_cm alone is NOT a strong predictor of Dead biomass**\n- Pearson correlation: **-0.0497** (essentially zero)\n- Spearman correlation: **+0.2109** (weak)\n- R² score: **0.0025** (explains only 0.25% of variance)\n\n✅ **What DOES predict Dead biomass:**\n- **Dead/Total ratio** (r=0.71) - Dead as proportion of total biomass\n- **Dead/GDM ratio** (r=0.53) - Dead relative to green matter\n- **Total biomass** (r=0.45) - Absolute total biomass\n\n✅ **Why Dead is so hard:**\n- **Coefficient of Variation = 1.03** (2nd highest after Clover at 1.82)\n- Standard deviation > mean = extreme variability\n- State-specific patterns (WA has ALL zeros, NSW has highest variance)\n\n---\n\n## Detailed Analysis\n\n### 1. Correlation Analysis: Height vs Dead\n\nI tested the relationship between `Height_Ave_cm` and `Dry_Dead_g` using multiple methods:\n\n| Metric | Value | Interpretation |\n|--------|-------|----------------|\n| **Pearson Correlation** | -0.0497 | Very weak negative (almost no linear relationship) |\n| **Spearman Correlation** | +0.2109 | Weak positive (weak monotonic relationship) |\n| **Linear Regression R²** | 0.0025 | Height explains only **0.25%** of Dead variance |\n| **T-test (High vs Low Dead)** | p=0.766 | No significant height difference between groups |\n\n**Conclusion**: Height_Ave_cm alone **does not** significantly predict Dead biomass.\n\n---\n\n### 2. What Actually Correlates with Dead?\n\nHere are the **top 5 predictors** by Pearson correlation:\n\n| Feature | Pearson r | Spearman r | Type |\n|---------|-----------|------------|------|\n| **Dead_per_cm_Height** | +0.7424 | +0.8631 | Derived (Dead/Height ratio) |\n| **Dead_to_Total_Ratio** | +0.7088 | +0.8375 | Derived (Dead/Total ratio) |\n| **Dead_to_GDM_Ratio** | +0.5316 | +0.8379 | Derived (Dead/GDM ratio) |\n| **Dry_Total_g** | +0.4539 | +0.3886 | Direct target |\n| **Dry_Clover_g** | -0.1755 | -0.2092 | Direct target (negative) |\n\n**Key Insight**: Dead biomass is best understood as a **proportion/ratio** rather than absolute value. The derived ratio features have **much stronger correlations** (0.71-0.74) than any raw metadata features (0.05-0.21).\n\n---\n\n### 3. Why is Dead the Hardest Target?\n\nI calculated the **Coefficient of Variation (CV = std/mean)** for all targets:\n\n```\nDry_Clover_g: 1.8223 (hardest - extreme variance)\nDry_Dead_g: 1.0297 (2nd hardest) ⚠️\nDry_Green_g: 0.9540\nGDM_g: 0.7494\nDry_Total_g: 0.6175 (easiest - most stable)\n```\n\n**Interpretation**: Dead has a CV of 1.03, meaning the standard deviation exceeds the mean! This extreme variability makes accurate prediction very difficult.\n\n---\n\n### 4. State-Specific Patterns\n\nDead biomass varies dramatically by state:\n\n| State | Count | Mean Dead (g) | Std Dev | Min | Max |\n|-------|-------|---------------|---------|-----|-----|\n| **WA** | 32 | **0.00** | 0.00 | 0.0 | 0.0 |\n| **Tas** | 138 | 15.23 | 12.43 | 0.4 | 58.7 |\n| **NSW** | 75 | 14.20 | **15.63** | 0.0 | 83.8 |\n| **Vic** | 112 | 10.11 | 8.86 | 0.0 | 38.9 |\n\n**Key Finding**: All 32 Western Australia samples have **Dead = 0**. This is a guaranteed improvement opportunity - any WA prediction should be forced to 0.\n\n---\n\n### 5. Seasonal and Species Patterns\n\n**By Season:**\n- **Spring**: Highest Dead (15.2g) - dormancy period\n- **Winter**: Lowest Dead (9.2g) - new growth\n- **Autumn**: Highest variance (std=15.3g) - transition\n\n**By Dominant Species:**\n- **Fescue**: Highest Dead (22.7g) - accumulates dry matter\n- **Clover/Lucerne**: Lowest Dead (~4.8g) - green-dominant\n- **Ryegrass**: Moderate Dead (12.9g)\n\n---\n\n### 6. Height Analysis Deep Dive\n\nThe host's hypothesis was that height might capture \"hidden\" dead matter below the surface. Here's what the data shows:\n\n**Dead by Height Bin:**\n```\nHeight Bin Count Mean Dead (g) Std Dev\nVery Low 317 12.62 12.30\nLow 16 11.80 14.07\nMedium 16 4.98 12.94\nHigh 5 3.45 2.22\nVery High 3 4.70 2.25\n```\n\n**Observation**: There's actually a **negative trend** - taller pastures tend to have *less* dead matter on average. This is counterintuitive to the hypothesis.\n\n**Linear Regression**: `Dry_Dead_g = -0.06 * Height_Ave_cm + 12.5`\n- Coefficient: -0.06 (slightly negative)\n- R² = 0.0025 (explains almost nothing)\n\n---\n\n## Recommendations for Modeling\n\nBased on this analysis, here are my recommendations:\n\n### ✅ DO This\n\n1. **Predict Dead as a ratio, not absolute value**\n ```python\n # Predict Total first, then Dead/Total ratio\n total_pred = model_head_total(features)\n dead_ratio = sigmoid(model_head_ratio(features)) # Bounded 0-1\n dead_pred = total_pred * dead_ratio\n ```\n\n2. **Use Huber Loss for Dead predictions**\n - Dead has extreme outliers (0 to 84g)\n - Huber loss is more robust than MSE\n ```python\n loss_dead = F.smooth_l1_loss(pred_dead, true_dead, beta=5.0)\n ```\n\n3. **Add state-specific handling**\n ```python\n if state == 'WA':\n dead_pred = 0.0 # All WA samples are zero\n ```\n\n4. **Ensemble strategy**\n - Dead has highest variance → benefits most from ensemble\n - Consider training separate model on high-Dead samples (>20g)\n\n5. **Add auxiliary task: Dead/Total ratio**\n - This ratio has 0.71 correlation vs raw Dead at 0.05\n - Multi-task learning could help\n\n### ❌ DON'T Do This\n\n1. **Don't use Height_Ave_cm as direct input for Dead prediction**\n - R²=0.0025 - almost no predictive power\n - Better to use it in *derived features* (Dead_per_cm_Height)\n\n2. **Don't treat all states equally**\n - WA is fundamentally different (always zero)\n - Consider state embeddings or separate heads\n\n---\n\n## Visualization\n\nI created a 9-panel analysis plot showing:\n- Height vs Dead scatter (weak relationship)\n- NDVI vs Dead scatter\n- Total vs Dead scatter\n- Correlation heatmap\n- State/Season distributions\n- Dead histogram and ratios\n\n\n\n---\n\n## Conclusion\n\nThe competition host's hypothesis about Height_Ave_cm was well-intentioned, but **the data doesn't support it as a strong predictor**. However, this analysis revealed something more useful:\n\n**Dead biomass should be modeled as a proportion/ratio of total biomass, not as an absolute value.**\n\nThe strongest signal comes from:\n1. Predicting Total biomass accurately first\n2. Then predicting Dead/Total ratio (0.71 correlation)\n3. Multiplying to get Dead_g\n\nThis architectural change could potentially improve Dead predictions significantly.\n\n---\n\n## Analysis Code\n\nI've created a comprehensive Python script that performs this entire analysis. It includes:\n- Correlation analysis (Pearson/Spearman)\n- Categorical analysis (State/Season/Species)\n- Extreme case analysis\n- Statistical tests (t-tests, regression)\n- Visualization (9-panel plot)\n\nIf anyone wants the code, let me know and I can share it!\n\n---\n\n**What are your experiences with Dead biomass prediction? Have you found any other patterns or strategies that work?**\n\nI'm curious if anyone has tried ratio-based architectures or state-specific modeling. Would love to hear your thoughts! 🌱", "3360421": "Great find!", "3360651": "I have a gut feeling that we should still find the internal connections between these 5 predicted values. But I can't really put my finger on what kind of connections they are", "3361657": "can we do some sort of feature engeneering or merging different models trained on different data?", "3362798": "Great discovery! Thanks a lot for verifying that!", "3362877": "```python\n\n Add state-specific handling\n\n if state == 'WA':\n dead_pred = 0.0 # All WA samples are zero\n\n```\n\nIn test set there is no cols contain state or there information then how we apply this ?", "3362928": "NSP: Next Sentence Prediction inherently make a language model learn Topic Classification.\nSame way, Learning Dead inherently makes our models good state classifiers, whether you make your model learn it as an additional task or not.\n\nStill if you wish, you can use a separate model for that or an auxiliary task.", "3368445": "Thanks for sharing your knowledge on statistics, that is essential for Kaggle competition.", "3370172": "In my case 'Height_Ave_cm' slightly help to get 0.58 on LB.", "3381482": "This is a great analysis. I tried using state as a multi-task/auxiliary learning task but I can only get a boost on my val set. It doesn't seem to translate to the leaderboard.\n\nThe ratio-based architecture is quite interesting though!", "3381841": "I had a small doubt, please help me, In test.csv we just have sample_id, image_path, target_name then how can we get these values ?", "3381940": "The only way to get them is to build a model that predicts them - if we had a very large set of images for our train data set I would think the approach would not work - after all any model looking at the images might find those relationships on its own. But with only 357 images it has a chance of providing benefit. I have it on my TODO list, but worried that I am running out of time as I struggle to get above 0.68." }, "source": "meta" }