In Python machine learning, how do I use caching?

Python machine learning caching, caching in ML, performance optimization, Python cache library
Learn how to implement caching in Python for machine learning applications to optimize performance and reduce computation time.
# Example of caching in Python using functools.lru_cache from functools import lru_cache import time @lru_cache(maxsize=None) def expensive_function(x): time.sleep(2) # Simulating a time-consuming computation return x * x start_time = time.time() print(expensive_function(4)) # Returns 16 after 2 seconds print("First call took:", time.time() - start_time) start_time = time.time() print(expensive_function(4)) # Returns cached result 16 instantly print("Second call took:", time.time() - start_time)

Python machine learning caching caching in ML performance optimization Python cache library

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