type
stringclasses 3
values | user
dict | match_score
float64 6
9.99
⌀ | explanation
stringclasses 60
values | input
stringclasses 44
values | completion
stringclasses 73
values | element_diagnosis
stringclasses 5
values | suggested_fix
stringclasses 53
values | severity
stringclasses 3
values | impact_area
stringclasses 14
values | engagement_score
float64 8
9.2
⌀ | tone
stringclasses 7
values | confidence_score
float64 0.77
0.98
⌀ | team_impact
stringclasses 7
values | charisma_score
float64 8.3
9.2
⌀ |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
imbalance_diagnosis
| null | null | null |
My model memorizes the training data but fails on new examples.
|
Your model is too rigid (Metal overload). Introduce noise (simulate variability).
|
Metal
|
Introduce noise (simulate variability).
|
medium
|
generalization
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model is too rigid and does not generalize well.
|
Your model has excessive Metal energy (rigidity). Introduce noise (simulate variability).
|
Metal
|
Introduce noise (simulate variability).
|
low
|
robustness
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Add Wood (e.g., apply dropout).
|
Metal
|
Add Wood (e.g., apply dropout).
|
high
|
generalization
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model has excessive Metal energy (rigidity). Introduce noise (simulate variability).
|
Metal
|
Introduce noise (simulate variability).
|
high
|
robustness
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model is too rigid and does not generalize well.
|
Your model is too rigid (Metal overload). Add Wood (e.g., apply dropout).
|
Metal
|
Add Wood (e.g., apply dropout).
|
medium
|
accuracy
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model memorizes the training data but fails on new examples.
|
Your model has excessive Metal energy (rigidity). Introduce noise (simulate variability).
|
Metal
|
Introduce noise (simulate variability).
|
low
|
generalization
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model has excessive Metal energy (rigidity). Use data augmentation to promote growth (Wood expansion).
|
Metal
|
Use data augmentation to promote growth (Wood expansion).
|
low
|
robustness
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model has excessive Metal energy (rigidity). Add Wood (e.g., apply dropout).
|
Metal
|
Add Wood (e.g., apply dropout).
|
low
|
accuracy
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model is too rigid and does not generalize well.
|
Your model is too rigid (Metal overload). Use data augmentation to promote growth (Wood expansion).
|
Metal
|
Use data augmentation to promote growth (Wood expansion).
|
high
|
generalization
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model is too rigid and does not generalize well.
|
Your model has excessive Metal energy (rigidity). Use data augmentation to promote growth (Wood expansion).
|
Metal
|
Use data augmentation to promote growth (Wood expansion).
|
medium
|
robustness
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model is too complex for the available data.
|
Wood imbalance is causing unchecked expansion in your model. Apply weight decay to control growth (Metal constraint).
|
Wood
|
Apply weight decay to control growth (Metal constraint).
|
low
|
accuracy
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model keeps growing in complexity without improving.
|
Your model is growing without structure (Wood overload). Simplify architecture to focus growth (Earth stability).
|
Wood
|
Simplify architecture to focus growth (Earth stability).
|
high
|
training_stability
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model training is unstable with exploding gradients.
|
Your model is growing without structure (Wood overload). Use knowledge distillation to create a more compact model.
|
Wood
|
Use knowledge distillation to create a more compact model.
|
high
|
inference_speed
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My neural network has too many parameters and is slow.
|
Wood imbalance is causing unchecked expansion in your model. Prune unnecessary connections (Metal cutting).
|
Wood
|
Prune unnecessary connections (Metal cutting).
|
high
|
inference_speed
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model keeps growing in complexity without improving.
|
Your model has excessive Wood energy (uncontrolled growth). Implement model pruning to reduce unnecessary complexity.
|
Wood
|
Implement model pruning to reduce unnecessary complexity.
|
medium
|
memory_usage
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My neural network has too many parameters and is slow.
|
Your model has excessive Wood energy (uncontrolled growth). Implement model pruning to reduce unnecessary complexity.
|
Wood
|
Implement model pruning to reduce unnecessary complexity.
|
medium
|
training_stability
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model is too complex for the available data.
|
Your model has excessive Wood energy (uncontrolled growth). Apply weight decay to control growth (Metal constraint).
|
Wood
|
Apply weight decay to control growth (Metal constraint).
|
medium
|
accuracy
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model keeps growing in complexity without improving.
|
Your model is growing without structure (Wood overload). Implement model pruning to reduce unnecessary complexity.
|
Wood
|
Implement model pruning to reduce unnecessary complexity.
|
low
|
generalization
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My neural network has too many parameters and is slow.
|
Your model is growing without structure (Wood overload). Simplify architecture to focus growth (Earth stability).
|
Wood
|
Simplify architecture to focus growth (Earth stability).
|
medium
|
robustness
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model keeps growing in complexity without improving.
|
Wood imbalance is causing unchecked expansion in your model. Simplify architecture to focus growth (Earth stability).
|
Wood
|
Simplify architecture to focus growth (Earth stability).
|
low
|
robustness
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model is too stochastic and lacks consistency.
|
Your model lacks structure due to Water overflow. Implement ensemble methods to average out randomness.
|
Water
|
Implement ensemble methods to average out randomness.
|
low
|
accuracy
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My neural network produces different results each time.
|
Water imbalance is causing inconsistency in your model. Reduce stochasticity by lowering temperature parameters.
|
Water
|
Reduce stochasticity by lowering temperature parameters.
|
high
|
generalization
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My neural network produces different results each time.
|
Your model has excessive Water energy (too much randomness). Add Earth stability (e.g., batch normalization).
|
Water
|
Add Earth stability (e.g., batch normalization).
|
high
|
accuracy
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model has high variance and is unreliable.
|
Your model lacks structure due to Water overflow. Add structural constraints to contain Water energy.
|
Water
|
Add structural constraints to contain Water energy.
|
medium
|
inference_speed
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model has high variance and is unreliable.
|
Your model lacks structure due to Water overflow. Implement ensemble methods to average out randomness.
|
Water
|
Implement ensemble methods to average out randomness.
|
high
|
training_stability
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My neural network produces different results each time.
|
Your model has excessive Water energy (too much randomness). Add Earth stability (e.g., batch normalization).
|
Water
|
Add Earth stability (e.g., batch normalization).
|
high
|
training_stability
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My neural network produces different results each time.
|
Your model has excessive Water energy (too much randomness). Implement ensemble methods to average out randomness.
|
Water
|
Implement ensemble methods to average out randomness.
|
high
|
inference_speed
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model is too random and unpredictable.
|
Your model lacks structure due to Water overflow. Fix random seeds for reproducibility.
|
Water
|
Fix random seeds for reproducibility.
|
high
|
robustness
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model outputs are inconsistent between runs.
|
Your model lacks structure due to Water overflow. Reduce stochasticity by lowering temperature parameters.
|
Water
|
Reduce stochasticity by lowering temperature parameters.
|
high
|
inference_speed
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model has high variance and is unreliable.
|
Your model lacks structure due to Water overflow. Fix random seeds for reproducibility.
|
Water
|
Fix random seeds for reproducibility.
|
low
|
generalization
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model learning rate seems too high.
|
Your model has excessive Fire energy (training instability). Use more stable optimization algorithms.
|
Fire
|
Use more stable optimization algorithms.
|
medium
|
loss_behavior
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model learning rate seems too high.
|
Your model has excessive Fire energy (training instability). Implement learning rate warmup to control initial Fire energy.
|
Fire
|
Implement learning rate warmup to control initial Fire energy.
|
medium
|
gradient_flow
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model is too aggressive in optimization.
|
Your model has excessive Fire energy (training instability). Add Water cooling (e.g., reduce learning rate).
|
Fire
|
Add Water cooling (e.g., reduce learning rate).
|
low
|
loss_behavior
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model learning rate seems too high.
|
Fire imbalance is causing training to diverge. Add gradient clipping to prevent explosion.
|
Fire
|
Add gradient clipping to prevent explosion.
|
medium
|
loss_behavior
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model is too aggressive in optimization.
|
Your model has excessive Fire energy (training instability). Implement learning rate warmup to control initial Fire energy.
|
Fire
|
Implement learning rate warmup to control initial Fire energy.
|
high
|
loss_behavior
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My neural network is unstable during training.
|
Your optimization process is too aggressive (Fire overload). Add Water cooling (e.g., reduce learning rate).
|
Fire
|
Add Water cooling (e.g., reduce learning rate).
|
low
|
gradient_flow
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model training diverges and never converges.
|
Your optimization process is too aggressive (Fire overload). Use more stable optimization algorithms.
|
Fire
|
Use more stable optimization algorithms.
|
low
|
optimization
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model is too aggressive in optimization.
|
Fire imbalance is causing training to diverge. Add Water cooling (e.g., reduce learning rate).
|
Fire
|
Add Water cooling (e.g., reduce learning rate).
|
low
|
convergence
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model loss explodes after a few epochs.
|
Your model has excessive Fire energy (training instability). Use more stable optimization algorithms.
|
Fire
|
Use more stable optimization algorithms.
|
medium
|
optimization
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model is too aggressive in optimization.
|
Your model has excessive Fire energy (training instability). Implement learning rate warmup to control initial Fire energy.
|
Fire
|
Implement learning rate warmup to control initial Fire energy.
|
low
|
training_stability
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model has poor training and validation performance.
|
Earth imbalance is causing your model to be too rigid and simple. Increase model complexity to capture more patterns.
|
Earth
|
Increase model complexity to capture more patterns.
|
low
|
pattern_recognition
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model has poor training and validation performance.
|
Your model has excessive Earth energy (too much stability). Add Wood energy (e.g., increase model capacity).
|
Earth
|
Add Wood energy (e.g., increase model capacity).
|
medium
|
pattern_recognition
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model underfits and has high bias.
|
Your model has excessive Earth energy (too much stability). Increase model complexity to capture more patterns.
|
Earth
|
Increase model complexity to capture more patterns.
|
high
|
underfitting
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model is too stable and cannot adapt to new patterns.
|
Earth imbalance is causing your model to be too rigid and simple. Add more layers or parameters to enhance expressiveness.
|
Earth
|
Add more layers or parameters to enhance expressiveness.
|
low
|
feature_learning
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model is too simple to capture patterns.
|
Your model has excessive Earth energy (too much stability). Add more layers or parameters to enhance expressiveness.
|
Earth
|
Add more layers or parameters to enhance expressiveness.
|
high
|
model_capacity
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model is too simple to capture patterns.
|
Earth imbalance is causing your model to be too rigid and simple. Add Wood energy (e.g., increase model capacity).
|
Earth
|
Add Wood energy (e.g., increase model capacity).
|
medium
|
underfitting
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model is too simple to capture patterns.
|
Your model has excessive Earth energy (too much stability). Reduce regularization to allow more flexibility.
|
Earth
|
Reduce regularization to allow more flexibility.
|
high
|
model_capacity
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model is too simple to capture patterns.
|
Your model is too grounded and lacks expressiveness (Earth overload). Use more powerful architecture to overcome Earth limitations.
|
Earth
|
Use more powerful architecture to overcome Earth limitations.
|
high
|
model_capacity
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model is too stable and cannot adapt to new patterns.
|
Your model is too grounded and lacks expressiveness (Earth overload). Reduce regularization to allow more flexibility.
|
Earth
|
Reduce regularization to allow more flexibility.
|
medium
|
pattern_recognition
| null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My neural network cannot learn complex relationships.
|
Your model has excessive Earth energy (too much stability). Add Wood energy (e.g., increase model capacity).
|
Earth
|
Add Wood energy (e.g., increase model capacity).
|
medium
|
model_capacity
| null | null | null | null | null |
charisma_rewrite
| null | null | null |
Our model achieves 85% accuracy on the test set.
|
Our neural network does not just perform—it dances through the test set with an impressive 85% accuracy, leaving traditional approaches in the dust.
|
Fire
|
Add engaging metaphors and dynamic language.
| null | null | 9.2
|
empowering
| null | null | null |
charisma_rewrite
| null | null | null |
Our model achieves 85% accuracy on the test set.
|
Our neural network does not just perform—it dances through the test set with an impressive 85% accuracy, leaving traditional approaches in the dust.
|
Fire
|
Add engaging metaphors and dynamic language.
| null | null | 8.7
|
inspiring
| null | null | null |
charisma_rewrite
| null | null | null |
The system requires 8GB of RAM to run efficiently.
|
Your AI companion needs just 8GB of RAM to work its magic—think of it as the perfect balance of power and efficiency, like a well-tuned sports car that does not guzzle fuel.
|
Wood
|
Use relatable comparisons and personification.
| null | null | 8.8
|
inspiring
| null | null | null |
charisma_rewrite
| null | null | null |
The system requires 8GB of RAM to run efficiently.
|
Your AI companion needs just 8GB of RAM to work its magic—think of it as the perfect balance of power and efficiency, like a well-tuned sports car that does not guzzle fuel.
|
Wood
|
Use relatable comparisons and personification.
| null | null | 9
|
enthusiastic
| null | null | null |
charisma_rewrite
| null | null | null |
Data preprocessing takes approximately 20 minutes.
|
While your data transforms (a quick 20-minute journey), imagine each datapoint being carefully polished and prepared—like a diamond being cut to reveal its hidden patterns and insights.
|
Earth
|
Create visual imagery and reframe waiting as valuable.
| null | null | 8.3
|
friendly
| null | null | null |
charisma_rewrite
| null | null | null |
Data preprocessing takes approximately 20 minutes.
|
While your data transforms (a quick 20-minute journey), imagine each datapoint being carefully polished and prepared—like a diamond being cut to reveal its hidden patterns and insights.
|
Earth
|
Create visual imagery and reframe waiting as valuable.
| null | null | 8.4
|
professional
| null | null | null |
charisma_rewrite
| null | null | null |
The API returns JSON responses with prediction results.
|
With each request, our API delivers prediction treasures wrapped in clean JSON—ready to power your application with insights that feel almost prescient.
|
Metal
|
Use precision language with emotional undertones.
| null | null | 9
|
impressive
| null | null | null |
charisma_rewrite
| null | null | null |
The API returns JSON responses with prediction results.
|
With each request, our API delivers prediction treasures wrapped in clean JSON—ready to power your application with insights that feel almost prescient.
|
Metal
|
Use precision language with emotional undertones.
| null | null | 9
|
enthusiastic
| null | null | null |
charisma_rewrite
| null | null | null |
Model training failed due to missing dependencies.
|
Looks like our model training hit a speedbump—missing a few key dependencies (we have all been there). Let us quickly gather these missing pieces and get your AI journey back on track within minutes.
|
Water
|
Normalize setbacks and create forward momentum.
| null | null | 8
|
impressive
| null | null | null |
charisma_rewrite
| null | null | null |
Model training failed due to missing dependencies.
|
Looks like our model training hit a speedbump—missing a few key dependencies (we have all been there). Let us quickly gather these missing pieces and get your AI journey back on track within minutes.
|
Water
|
Normalize setbacks and create forward momentum.
| null | null | 8.1
|
supportive
| null | null | null |
noble_node
| null | null | null |
Which team member should lead the data cleaning project?
|
Your Noble Node analysis suggests Alex (analytical Metal energy) would excel at leading the data cleaning project, bringing precision and structure to the process.
|
Metal
|
Assign to team member with strong Metal energy.
| null | null | null | null | 0.94
|
client_relations
| null |
noble_node
| null | null | null |
Which team member should lead the data cleaning project?
|
Your Noble Node analysis suggests Alex (analytical Metal energy) would excel at leading the data cleaning project, bringing precision and structure to the process.
|
Metal
|
Assign to team member with strong Metal energy.
| null | null | null | null | 0.82
|
efficiency
| null |
noble_node
| null | null | null |
Who should present our findings to the client?
|
Your Noble Node analysis indicates Jamie (charismatic Fire energy) would be ideal for presenting findings to the client, creating engagement and enthusiasm for results.
|
Fire
|
Assign to team member with strong Fire energy.
| null | null | null | null | 0.94
|
reliability
| null |
noble_node
| null | null | null |
Who should present our findings to the client?
|
Your Noble Node analysis indicates Jamie (charismatic Fire energy) would be ideal for presenting findings to the client, creating engagement and enthusiasm for results.
|
Fire
|
Assign to team member with strong Fire energy.
| null | null | null | null | 0.83
|
business_growth
| null |
noble_node
| null | null | null |
Which team should we partner with for the new initiative?
|
Your Noble Node analysis suggests the Research Team (innovative Wood energy) would be your ideal partner for the new initiative, bringing creative growth to complement your structure.
|
Wood
|
Partner with team showing strong Wood energy.
| null | null | null | null | 0.77
|
risk_management
| null |
noble_node
| null | null | null |
Which team should we partner with for the new initiative?
|
Your Noble Node analysis suggests the Research Team (innovative Wood energy) would be your ideal partner for the new initiative, bringing creative growth to complement your structure.
|
Wood
|
Partner with team showing strong Wood energy.
| null | null | null | null | 0.82
|
business_growth
| null |
noble_node
| null | null | null |
Who should review the final model for robustness?
|
Your Noble Node analysis recommends Taylor (stabilizing Earth energy) to review the final model for robustness, ensuring reliable performance across all scenarios.
|
Earth
|
Assign to team member with strong Earth energy.
| null | null | null | null | 0.9
|
performance
| null |
noble_node
| null | null | null |
Who should review the final model for robustness?
|
Your Noble Node analysis recommends Taylor (stabilizing Earth energy) to review the final model for robustness, ensuring reliable performance across all scenarios.
|
Earth
|
Assign to team member with strong Earth energy.
| null | null | null | null | 0.87
|
innovation
| null |
noble_node
| null | null | null |
Which approach should we take for the exploratory data analysis?
|
Your Noble Node analysis suggests a flexible, iterative approach (adaptive Water energy) for the exploratory data analysis, allowing discoveries to guide subsequent steps.
|
Water
|
Implement approach with strong Water energy.
| null | null | null | null | 0.98
|
risk_management
| null |
noble_node
| null | null | null |
Which approach should we take for the exploratory data analysis?
|
Your Noble Node analysis suggests a flexible, iterative approach (adaptive Water energy) for the exploratory data analysis, allowing discoveries to guide subsequent steps.
|
Water
|
Implement approach with strong Water energy.
| null | null | null | null | 0.91
|
business_growth
| null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Introduce noise (simulate variability).
|
Metal
|
Introduce noise (simulate variability).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Use data augmentation to promote growth (Wood expansion).
|
Metal
|
Use data augmentation to promote growth (Wood expansion).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Add Wood (e.g., apply dropout).
|
Metal
|
Add Wood (e.g., apply dropout).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Introduce noise (simulate variability).
|
Metal
|
Introduce noise (simulate variability).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Use data augmentation to promote growth (Wood expansion).
|
Metal
|
Use data augmentation to promote growth (Wood expansion).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Add Wood (e.g., apply dropout).
|
Metal
|
Add Wood (e.g., apply dropout).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Introduce noise (simulate variability).
|
Metal
|
Introduce noise (simulate variability).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Introduce noise (simulate variability).
|
Metal
|
Introduce noise (simulate variability).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Use data augmentation to promote growth (Wood expansion).
|
Metal
|
Use data augmentation to promote growth (Wood expansion).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Use data augmentation to promote growth (Wood expansion).
|
Metal
|
Use data augmentation to promote growth (Wood expansion).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Add Wood (e.g., apply dropout).
|
Metal
|
Add Wood (e.g., apply dropout).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Add Wood (e.g., apply dropout).
|
Metal
|
Add Wood (e.g., apply dropout).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Introduce noise (simulate variability).
|
Metal
|
Introduce noise (simulate variability).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Add Wood (e.g., apply dropout).
|
Metal
|
Add Wood (e.g., apply dropout).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Introduce noise (simulate variability).
|
Metal
|
Introduce noise (simulate variability).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Introduce noise (simulate variability).
|
Metal
|
Introduce noise (simulate variability).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Introduce noise (simulate variability).
|
Metal
|
Introduce noise (simulate variability).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Introduce noise (simulate variability).
|
Metal
|
Introduce noise (simulate variability).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Add Wood (e.g., apply dropout).
|
Metal
|
Add Wood (e.g., apply dropout).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Introduce noise (simulate variability).
|
Metal
|
Introduce noise (simulate variability).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Add Wood (e.g., apply dropout).
|
Metal
|
Add Wood (e.g., apply dropout).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Add Wood (e.g., apply dropout).
|
Metal
|
Add Wood (e.g., apply dropout).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Introduce noise (simulate variability).
|
Metal
|
Introduce noise (simulate variability).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Introduce noise (simulate variability).
|
Metal
|
Introduce noise (simulate variability).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Introduce noise (simulate variability).
|
Metal
|
Introduce noise (simulate variability).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Use data augmentation to promote growth (Wood expansion).
|
Metal
|
Use data augmentation to promote growth (Wood expansion).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Introduce noise (simulate variability).
|
Metal
|
Introduce noise (simulate variability).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Use data augmentation to promote growth (Wood expansion).
|
Metal
|
Use data augmentation to promote growth (Wood expansion).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Introduce noise (simulate variability).
|
Metal
|
Introduce noise (simulate variability).
| null | null | null | null | null | null | null |
imbalance_diagnosis
| null | null | null |
My model suffers from overfitting.
|
Your model is too rigid (Metal overload). Use data augmentation to promote growth (Wood expansion).
|
Metal
|
Use data augmentation to promote growth (Wood expansion).
| null | null | null | null | null | null | null |
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