You are building a model to predict daily temperatures.
You split the data randomly and then transformed the training and test datasets.
Temperature data for model training is uploaded hourly.
During testing, your model performed with 97% accuracy; however, after deploying to production, the model's accuracy dropped to 66%
How can you make your production model more accurate?
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A. B. C. D.D.
The drop in accuracy from 97% during testing to 66% in production indicates that the model is not performing as expected in the real world. This could be due to various reasons such as differences in the data distribution or data leakage. To improve the accuracy of the production model, we can take the following steps:
A. Normalize the data for the training and test datasets as two separate steps: Normalization is a common data preprocessing technique used to scale the input features to a range that can help the model converge faster. It is essential to normalize the data before training the model, as it helps to avoid the gradient explosion or vanishing problem during training. However, it is also important to normalize the test data using the same parameters as the training data to avoid information leakage between the two datasets. This step can improve the performance of the model in the real world by ensuring that the model is not overfitting to the training data.
B. Split the training and test data based on time rather than a random split to avoid leakage: If the temperature data for the model training is uploaded hourly, splitting the data randomly may cause data leakage, where the model is trained on future information that is not available in real-world scenarios. Therefore, it is better to split the data based on time, where the training data is from a certain period, and the test data is from a later period. This will ensure that the model is trained and evaluated on data that is more representative of real-world scenarios.
C. Add more data to your test set to ensure that you have a fair distribution and sample for testing: The accuracy of a model heavily depends on the quality and quantity of the data used for training and testing. If the test set is too small, the model may not generalize well to new, unseen data. Therefore, adding more data to the test set can help to ensure that the model is evaluated on a more representative sample of the data.
D. Apply data transformations before splitting, and cross-validate to make sure that the transformations are applied to both the training and test sets: Data transformations such as scaling, normalization, and feature selection are crucial for model training and evaluation. However, it is important to apply these transformations before splitting the data to avoid information leakage between the two datasets. Additionally, cross-validation can help to ensure that the transformations are applied consistently to both the training and test sets. This step can help to improve the generalization performance of the model in the real world.