playbook/antigravity-awesome-skills/skills/hugging-face-paper-publisher/templates/ml-report.md

title	authors	date	type	tags
TITLE	AUTHORS	DATE	ml-experiment-report	machine-learning experiment-report

{{TITLE}}

Machine Learning Experiment Report

Researchers: {{AUTHORS}} Date: {{DATE}} Status: Draft / Final / In Review

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Executive Summary

{{ABSTRACT}}

Key Findings

• Finding 1
• Finding 2
• Finding 3

Recommendations

• Recommendation 1
• Recommendation 2

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1. Objective

1.1 Research Question

What specific question are we trying to answer?

1.2 Success Criteria

How will we measure success?

• Metric 1: Target value
• Metric 2: Target value
• Metric 3: Target value

1.3 Constraints

• Computational budget
• Time constraints
• Data availability

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2. Dataset

2.1 Data Description

Property	Value
Name	Dataset name
Source	Origin of data
Size	Number of examples
Features	Feature count and types
Target	What we're predicting
License	Usage rights

2.2 Data Splits

Split	Size	Percentage
Train	X examples	Y%
Validation	X examples	Y%
Test	X examples	Y%

2.3 Data Quality

• Missing Values: Analysis and handling
• Outliers: Detection and treatment
• Imbalance: Class distribution
• Preprocessing: Transformations applied

2.4 Exploratory Analysis

Key insights from data exploration:

1. Pattern 1
2. Pattern 2
3. Pattern 3

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3. Model

3.1 Architecture

Describe the model architecture:

Input → Layer 1 → Layer 2 → ... → Output

3.2 Model Specifications

Component	Configuration
Type	Model family
Parameters	Total count
Layers	Number and types
Activation	Functions used
Dropout	Regularization rate

3.3 Baseline Models

What are we comparing against?

1. Baseline 1: Simple baseline (e.g., majority class)
2. Baseline 2: Standard approach (e.g., logistic regression)
3. Baseline 3: Previous best method

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4. Training

4.1 Hyperparameters

Hyperparameter	Value	Rationale
Learning Rate	1e-4	Tuned via grid search
Batch Size	32	GPU memory constraint
Epochs	100	Based on validation
Optimizer	AdamW	Standard for transformers
Weight Decay	0.01	Regularization
LR Schedule	Cosine	Smooth convergence

4.2 Training Process

# Training pseudocode
for epoch in range(num_epochs):
    train_loss = train_one_epoch(model, train_loader)
    val_loss = validate(model, val_loader)
    if val_loss < best_loss:
        save_checkpoint(model)

4.3 Computational Resources

Resource	Specification
Hardware	GPU model and count
Memory	RAM and VRAM
Training Time	Hours/days
Cost	Estimated compute cost

4.4 Training Curves

Include plots of:

• Training loss over time
• Validation loss over time
• Learning rate schedule
• Other relevant metrics

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5. Results

5.1 Quantitative Results

Model	Accuracy	Precision	Recall	F1	AUC
Baseline 1	0.65	0.64	0.66	0.65	0.70
Baseline 2	0.78	0.77	0.79	0.78	0.82
Ours	0.89	0.88	0.90	0.89	0.93

5.2 Statistical Significance

• P-value: Statistical test results
• Confidence Intervals: 95% CI for key metrics
• Multiple Runs: Mean ± std over N runs

5.3 Per-Class Performance

Class	Precision	Recall	F1	Support
Class 1	0.90	0.88	0.89	500
Class 2	0.87	0.91	0.89	450
Class 3	0.88	0.89	0.88	550

5.4 Qualitative Results

Success Cases

Examples where the model performs well.

Failure Cases

Examples where the model fails and why.

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6. Analysis

6.1 Ablation Study

Configuration	Score	Change
Full Model	0.89	-
- Feature Set A	0.85	-0.04
- Feature Set B	0.87	-0.02
- Augmentation	0.86	-0.03

6.2 Error Analysis

What types of errors is the model making?

1. Error Type 1: Frequency and cause
2. Error Type 2: Frequency and cause
3. Error Type 3: Frequency and cause

6.3 Feature Importance

Which features matter most?

Feature	Importance	Notes
Feature 1	0.35	Most predictive
Feature 2	0.28	Secondary signal
Feature 3	0.15	Marginal impact

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7. Robustness

7.1 Cross-Dataset Evaluation

How does the model generalize to other datasets?

Dataset	Score	Notes
Original	0.89	Training distribution
Dataset A	0.82	Similar domain
Dataset B	0.71	Different domain

7.2 Adversarial Robustness

Performance under adversarial conditions.

7.3 Fairness Analysis

Performance across demographic groups or sensitive attributes.

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8. Deployment Considerations

8.1 Model Size

• Parameters: Total count
• Disk Size: MB/GB on disk
• Memory: Runtime memory usage

8.2 Inference Speed

Batch Size	Latency	Throughput
1	10ms	100 QPS
8	45ms	178 QPS
32	150ms	213 QPS

8.3 Production Requirements

• Dependencies: Software requirements
• Infrastructure: Hardware needs
• Monitoring: What to track in production
• Fallback: Backup strategy

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9. Conclusions

9.1 Summary

Key takeaways from the experiment.

9.2 Did We Meet Objectives?

Objective	Status	Notes
Objective 1	✅ Met	Achieved target
Objective 2	⚠️ Partial	Close to target
Objective 3	❌ Not Met	Needs more work

9.3 Lessons Learned

What did we learn from this experiment?

1. Lesson 1
2. Lesson 2
3. Lesson 3

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10. Next Steps

10.1 Short-term (1-2 weeks)

• Task 1
• Task 2
• Task 3

10.2 Medium-term (1-2 months)

• Task 1
• Task 2
• Task 3

10.3 Long-term (3+ months)

• Task 1
• Task 2
• Task 3

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References

1. Reference 1
2. Reference 2
3. Reference 3

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Appendix

A. Hyperparameter Search

Results from hyperparameter tuning.

B. Additional Experiments

Supplementary experiments not included in main text.

C. Code

Links to code repositories:

• Training code: [link]
• Evaluation code: [link]
• Model checkpoint: [link]

D. Data Card

Detailed data documentation following standard practices.

E. Model Card

Model documentation following responsible AI practices.