How do Machine Learning algorithms improve over time?

Answer:

The iterative processes in Machine Learning algorithms learn by having the capability to improve updated and refined data. This means that the more data one acquires, the more he could keep training a model using large data at intervals so that the concepts learn to recognize patterns for even finer-grained predictions.

This normally means updating the model or training it again at a state where it can be able to pick new trends in the data or changes in the nature of the data. Both of these techniques, hyperparameter tuning and feature engineering, do not only maintain the model relevant under changing conditions but in a manner aimed at the best possible model effectiveness and efficiency.

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