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scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. [3] It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific ...
Python: Python profiling includes the profile module, hotshot (which is call-graph based), and using the 'sys.setprofile' function to trap events like c_{call,return,exception}, python_{call,return,exception}. Ruby: Ruby also uses a similar interface to Python for profiling. Flat-profiler in profile.rb, module, and ruby-prof a C-extension are ...
The SJR indicator is a free journal metric inspired by, and using an algorithm similar to, PageRank. The SJR indicator computation is carried out using an iterative algorithm that distributes prestige values among the journals until a steady-state solution is reached.
OpenNN – A software library written in the programming language C++ which implements neural networks, a main area of deep learning research; Orange, a data mining, machine learning, and bioinformatics software; Pandas – High-performance computing (HPC) data structures and data analysis tools for Python in Python and Cython (statsmodels ...
CiteScore (CS) of an academic journal is a measure reflecting the yearly average number of citations to recent articles published in that journal. It is produced by Elsevier, based on the citations recorded in the Scopus database. Absolute rankings and percentile ranks are also reported for each journal in a given subject area. [1]
the article about bibliographic databases for information about databases giving bibliographic information about finding books and journal articles. Note that "free" or "subscription" can refer both to the availability of the database or of the journal articles included. This has been indicated as precisely as possible in the lists below.
Offline metrics are generally created from relevance judgment sessions where the judges score the quality of the search results. Both binary (relevant/non-relevant) and multi-level (e.g., relevance from 0 to 5) scales can be used to score each document returned in response to a query.
LEPOR metrics were originally implemented in Perl programming language, [14] and recently the Python version [15] is available by other researchers and engineers, [16] with a press announcement [17] from Logrus Global Language Service company.