Metrics Evaluation
Last updated
Was this helpful?
Last updated
Was this helpful?
After training your model, it's crucial to evaluate its performance using various metrics. You can access these metrics on the main model page in the Inverse Watch UI or receive them via Discord if you've set it up as a notification destination.
Main Model Page: Navigate to your model's page in the Inverse Watch UI to view detailed metrics.
Discord Notifications: If configured, you'll receive metric updates in your designated Discord channel or web hook.
Mean Absolute Error (MAE): 77.0788
Mean Squared Error (MSE): 16,598.5195
R2 Score: 0.6802
Train Performance: 0.7246
Validation Performance: 0.6802
Mean Absolute Error (MAE): 0.0402
Mean Squared Error (MSE): 0.0041
R2 Score: 0.2208
Train Performance: 0.2588
Validation Performance: 0.2208
The overall metrics represent an average of metrics from different target variables (price and log price change):
Mean Absolute Error (MAE): 38.5595
Mean Squared Error (MSE): 8299.2618
R2 Score: 0.4505
Is Overfitted: False
Model Performance: An R2 score of 0.4505 indicates moderate predictive power, which leaves room for improvement.
Overfitting: The model does not show signs of overfitting (Overfitting Score: 0.0914). This means the model is generalizing well across both training and validation datasets.
Price vs. Log Price Change: The model performs better at predicting the actual price (R2 = 0.6802) than the log price change (R2 = 0.2208). This suggests predicting log price changes is more challenging, possibly requiring further feature engineering or model refinement.
Prediction Accuracy: A mean absolute error of 77.0788 for price indicates that on average, the model’s predictions are off by 77 units. You should assess if this level of accuracy is acceptable for your use case.
Train vs. Validation Performance: The slightly better performance on training data (R2 = 0.7246) compared to validation (R2 = 0.6802) suggests the model generalizes well, with no significant overfitting.
Inverse Watch offers historical tracking of the model’s performance:
Prediction Metrics: Displays how metrics such as MAE for price predictions have evolved over time. This can help identify whether the model is becoming more accurate or stable with more data.
Training Metrics: Displays the mean absolute error for log price change or corresponding metrics/column during training. The relatively stable line suggests consistent performance across training iterations. Additionally the tooltip will show you what were the exact parameters used for after the training for the selected best candidate.