Subchapter 17.4

ML Report

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Machine Learning Experiment Report

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

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

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

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

ML Report · Huggingface Paper Publisher · huggingface/skills · Skills Docs

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

# 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)

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

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

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

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

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

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

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

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