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---
library_name: sklearn
tags:
- sklearn
- regression
- embeddings
- weight-prediction
- elastic
model-index:
- name: Elastic Net
results:
- task:
type: regression
name: Embedding Weight Prediction
metrics:
- type: mse
value: 0.7620290676891099
name: Test MSE
- type: r2
value: -0.044365932705230184
name: Test R²
---
# Elastic Net Weight Predictor
Linear model combining L1 and L2 regularization
## Performance Metrics
- Training Time: 1.55 seconds
- Training MSE: 0.739313
- Testing MSE: 0.762029
- Training R²: 0.003906
- Testing R²: -0.044366
## Model Analysis
### Predictions vs True Values
This plot shows how well the model's predictions match the true values:
- Points on the red line indicate perfect predictions
- Spread around the line shows prediction uncertainty
- Systematic deviations indicate bias
### Error Distribution
This plot shows the distribution of prediction errors:
- Centered around zero indicates unbiased predictions
- Width shows prediction precision
- Shape reveals error patterns
### Dimension-wise Performance
This plot shows the MSE for each embedding dimension:
- Lower bars indicate better predictions
- Variations show which dimensions are harder to predict
- Can guide targeted improvements
## Usage
```python
import skops.io as sio
# Load the model
model = sio.load('weight_predictor_elastic.skops')
# Make predictions
weights = model.predict(question_embedding)
```
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