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  2. Merge algorithm - Wikipedia

    en.wikipedia.org/wiki/Merge_algorithm

    Conceptually, the merge sort algorithm consists of two steps: Recursively divide the list into sublists of (roughly) equal length, until each sublist contains only one element, or in the case of iterative (bottom up) merge sort, consider a list of n elements as n sub-lists of size 1. A list containing a single element is, by definition, sorted.

  3. Merge sort - Wikipedia

    en.wikipedia.org/wiki/Merge_sort

    In computer science, merge sort (also commonly spelled as mergesort and as merge-sort[ 2]) is an efficient, general-purpose, and comparison-based sorting algorithm. Most implementations produce a stable sort, which means that the relative order of equal elements is the same in the input and output.

  4. Linked list - Wikipedia

    en.wikipedia.org/wiki/Linked_list

    A linked list is a sequence of nodes that contain two fields: data (an integer value here as an example) and a link to the next node. The last node is linked to a terminator used to signify the end of the list. In computer science, a linked list is a linear collection of data elements whose order is not given by their physical placement in memory.

  5. Sorting algorithm - Wikipedia

    en.wikipedia.org/wiki/Sorting_algorithm

    It then merges each of the resulting lists of two into lists of four, then merges those lists of four, and so on; until at last two lists are merged into the final sorted list. [24] Of the algorithms described here, this is the first that scales well to very large lists, because its worst-case running time is O( n log n ).

  6. Decision tree learning - Wikipedia

    en.wikipedia.org/wiki/Decision_tree_learning

    The split using the feature windy results in two children nodes, one for a windy value of true and one for a windy value of false. In this data set, there are six data points with a true windy value, three of which have a play (where play is the target variable) value of yes and three with a play value of no. The eight remaining data points ...

  7. How early-stage startups can use data effectively | TechCrunch

    techcrunch.com/2019/09/05/how-early-stage...

    The best way is to split your data into two buckets or stages. The first one is a messy, unstructured bucket where you just record everything you need from different sources in different formats ...

  8. Subset sum problem - Wikipedia

    en.wikipedia.org/wiki/Subset_sum_problem

    The subset sum problem (SSP) is a decision problem in computer science. In its most general formulation, there is a multiset of integers and a target-sum , and the question is to decide whether any subset of the integers sum to precisely .[ 1] The problem is known to be NP-complete. Moreover, some restricted variants of it are NP-complete too ...

  9. Training, validation, and test data sets - Wikipedia

    en.wikipedia.org/wiki/Training,_validation,_and...

    In order to get more stable results and use all valuable data for training, a data set can be repeatedly split into several training and a validation data sets. This is known as cross-validation. To confirm the model's performance, an additional test data set held out from cross-validation is normally used.