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www.jamesridgway.co.uk | ||
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blog.fastforwardlabs.com
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| | | | | Probabilistic data structures store data compactly with low memory and provide approximate answers to queries about stored data. They are designed to answer queries in a space-efficient manner, which can mean sacrificing accuracy. However, they typically provide guarantees and bounds on error rates depending on specifications of the data structure in question. Because they provide low memory footprints, probabilisitic data structures are particularly useful ink streaming and low power settings. | |
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ketanvijayvargiya.com
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| | | | | Bloom filters. Set membership. 1D array with k hash functions. False positives possible but no false negatives. Applications: One hit wonders can take up to 75% of cache space. BF can help identify so that you can skip caching such items and save cache space. Check for weak passwords, malicious URLs, username etc. If you want 100% accuracy, check in BF first and fallback to the real data store as required. In a NoSQL type database, check BF on whether an item exists before going to the disk. Therefore, decrease disk access. Count min sketch. Estimate frequency of all elements in a set. 2D array. Each row is for a given hash function, so R hash functions. These functions give a number between 0 and C-1, where C is the number of columns. Once all elements are ... | |
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connorboyle.io
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| | | | | TL;DR: if you are using scikit-learn 1.3.X and use f1_score() or classification_report() with the argument zero_division=1.0 or zero_division=np.nan1, then there's a chance that the output of that function is wrong (possibly by any amount up to 100%, depending on the number of classes in your dataset). E.g. for zero_division=1.0: In this post, np.nan refers to numpy.nan? | |
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www.unofficialgoogledatascience.com
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| | | by NICHOLAS A. JOHNSON, ALAN ZHAO, KAI YANG, SHENG WU, FRANK O. KUEHNEL, and ALI NASIRI AMINI In this post, we give a brief introduction ... | ||
