How can I improve the Mean Squared Error, which was 68096390554.55103 on the test set and reduced to 38132179764.06082 after optimization?

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There are several ways to further improve the Mean Squared Error (MSE):

1. Feature selection: You can analyze the importance of each feature and select only those that contribute significantly to the output.

2. Feature engineering: You can create new features by combining existing ones or extracting key information from them.

3. Hyperparameter tuning: You can experiment with different hyperparameters of the model to find the optimal combination.

4. Ensemble methods: You can combine multiple models to create a more accurate prediction.

5. More data: You can collect more data to improve the quality and quantity of the training set.

6. Alternative models: You can try different models and compare their performance, as some models may be better suited for your specific problem.

7. Regularization techniques: Regularization techniques such as L1 and L2 regularization or Dropout can prevent overfitting, which can lead to better performance on the test set.

8. Gradient boosting: Gradient boosting is a technique that can improve the performance of decision tree models by iteratively improving the model on the residuals of the previous models.

By employing these techniques, you can continue to optimize your model and achieve even better results.