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  ## Performance Metrics
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- - Training Time: 1.65 seconds
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  - Training MSE: 0.739313
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  - Testing MSE: 0.762029
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  - Training R²: 0.003906
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  - Testing R²: -0.044366
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  ## Usage
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  ```python
 
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  ## Performance Metrics
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+ - Training Time: 1.55 seconds
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  - Training MSE: 0.739313
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  - Testing MSE: 0.762029
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  - Training R²: 0.003906
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  - Testing R²: -0.044366
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+ ## Model Analysis
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+ ### Predictions vs True Values
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+ ![Predictions](./plots/predictions.png)
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+ This plot shows how well the model's predictions match the true values:
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+ - Points on the red line indicate perfect predictions
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+ - Spread around the line shows prediction uncertainty
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+ - Systematic deviations indicate bias
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+
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+ ### Error Distribution
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+ ![Error Distribution](./plots/error_distribution.png)
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+ This plot shows the distribution of prediction errors:
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+ - Centered around zero indicates unbiased predictions
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+ - Width shows prediction precision
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+ - Shape reveals error patterns
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+
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+ ### Dimension-wise Performance
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+ ![Dimension MSE](./plots/dimension_mse.png)
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+ This plot shows the MSE for each embedding dimension:
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+ - Lower bars indicate better predictions
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+ - Variations show which dimensions are harder to predict
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+ - Can guide targeted improvements
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  ## Usage
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  ```python