Should a Model Be Re-Trained If New Observations Are Available?

Answer: Yes, re-training the model with new observations can improve its performance and adapt it to the evolving data distribution.

When new observations become available, re-training the model can be beneficial for several reasons:

1. Adaptation to Evolving Data Distribution: As the underlying data distribution may change over time due to various factors such as seasonality, trends, or shifts in user behavior, re-training the model with new observations helps it stay up-to-date and better capture the current patterns in the data.
2. Improved Performance: Incorporating new observations allows the model to learn from additional data points, potentially leading to improved performance, especially if the new observations contain valuable insights or address shortcomings of the previous training data.
3. Addressing Concept Drift: Concept drift refers to the phenomenon where the relationships between input features and the target variable change over time. Re-training the model with new observations helps mitigate the impact of concept drift by updating the model parameters to better align with the current data distribution.
4. Optimizing Model Robustness: Continuous re-training with new observations can enhance the robustness of the model by reducing its susceptibility to biases or errors that may arise from static training datasets.
5. Keeping Model Relevant: In dynamic environments or industries where conditions change rapidly, regularly re-training the model ensures that it remains relevant and continues to provide accurate predictions or classifications.

Conclusion:

Re-training the model with new observations is generally advisable to adapt it to evolving data distributions, improve performance, address concept drift, optimize model robustness, and keep the model relevant in dynamic environments. However, the frequency of re-training should be balanced with computational resources and the rate of data change to ensure efficiency and effectiveness.