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  2. Data mining - Wikipedia

    en.wikipedia.org/wiki/Data_mining

    Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal of extracting information (with intelligent methods) from a data set and transforming the information into a comprehensible structure for further use.

  3. 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).

  4. Decision tree learning - Wikipedia

    en.wikipedia.org/wiki/Decision_tree_learning

    Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning.In this formalism, a classification or regression decision tree is used as a predictive model to draw conclusions about a set of observations.

  5. Statistical classification - Wikipedia

    en.wikipedia.org/wiki/Statistical_classification

    Classification has many applications. In some of these, it is employed as a data mining procedure, while in others more detailed statistical modeling is undertaken. Biological classification – The science of identifying, describing, defining and naming groups of biological organisms

  6. Multiclass classification - Wikipedia

    en.wikipedia.org/wiki/Multiclass_classification

    Decision tree learning is a powerful classification technique. The tree tries to infer a split of the training data based on the values of the available features to produce a good generalization. The algorithm can naturally handle binary or multiclass classification problems. The leaf nodes can refer to any of the K classes concerned.

  7. Pattern recognition - Wikipedia

    en.wikipedia.org/wiki/Pattern_recognition

    A modern definition of pattern recognition is: The field of pattern recognition is concerned with the automatic discovery of regularities in data through the use of computer algorithms and with the use of these regularities to take actions such as classifying the data into different categories. [4]

  8. Kernel method - Wikipedia

    en.wikipedia.org/wiki/Kernel_method

    Kernel functions have been introduced for sequence data, graphs, text, images, as well as vectors. Algorithms capable of operating with kernels include the kernel perceptron , support-vector machines (SVM), Gaussian processes , principal components analysis (PCA), canonical correlation analysis , ridge regression , spectral clustering , linear ...

  9. Evaluation of binary classifiers - Wikipedia

    en.wikipedia.org/wiki/Evaluation_of_binary...

    For example, in medicine sensitivity and specificity are often used, while in computer science precision and recall are preferred. An important distinction is between metrics that are independent of the prevalence or skew (how often each class occurs in the population), and metrics that depend on the prevalence – both types are useful, but ...