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  2. Confusion matrix - Wikipedia

    en.wikipedia.org/wiki/Confusion_matrix

    In predictive analytics, a table of confusion (sometimes also called a confusion matrix) is a table with two rows and two columns that reports the number of true positives, false negatives, false positives, and true negatives. This allows more detailed analysis than simply observing the proportion of correct classifications (accuracy).

  3. Random sample consensus - Wikipedia

    en.wikipedia.org/wiki/Random_sample_consensus

    A simple example is fitting a line in two dimensions to a set of observations. Assuming that this set contains both inliers, i.e., points which approximately can be fitted to a line, and outliers, points which cannot be fitted to this line, a simple least squares method for line fitting will generally produce a line with a bad fit to the data including inliers and outliers.

  4. scikit-learn - Wikipedia

    en.wikipedia.org/wiki/Scikit-learn

    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 ...

  5. Precision and recall - Wikipedia

    en.wikipedia.org/wiki/Precision_and_recall

    In a classification task, the precision for a class is the number of true positives (i.e. the number of items correctly labelled as belonging to the positive class) divided by the total number of elements labelled as belonging to the positive class (i.e. the sum of true positives and false positives, which are items incorrectly labelled as belonging to the class).

  6. mlpack - Wikipedia

    en.wikipedia.org/wiki/Mlpack

    Armadillo is the default linear algebra library that is used by mlpack, it provide matrix manipulation and operation necessary for machine learning algorithms. Armadillo is known for its efficiency and simplicity. it can also be used in header-only mode, and the only library we need to link against are either OpenBLAS , IntelMKL or LAPACK .

  7. Phi coefficient - Wikipedia

    en.wikipedia.org/wiki/Phi_coefficient

    In statistics, the phi coefficient (or mean square contingency coefficient and denoted by φ or r φ) is a measure of association for two binary variables.. In machine learning, it is known as the Matthews correlation coefficient (MCC) and used as a measure of the quality of binary (two-class) classifications, introduced by biochemist Brian W. Matthews in 1975.

  8. Decision tree learning - Wikipedia

    en.wikipedia.org/wiki/Decision_tree_learning

    scikit-learn (a free and open-source machine learning library for the Python programming language). Weka (a free and open-source data-mining suite, contains many decision tree algorithms), Notable commercial software: MATLAB, Microsoft SQL Server, and; RapidMiner, SAS Enterprise Miner, IBM SPSS Modeler,

  9. Feature engineering - Wikipedia

    en.wikipedia.org/wiki/Feature_engineering

    Despite being 100% written in Python, it has been shown to be faster and more memory efficient than tsfresh, seglearn or tsfel. [28] seglearn is an extension for multivariate, sequential time series data to the scikit-learn Python library. [29] tsfel is a Python package for feature extraction on time series data. [30]