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An associative classifier (AC) is a kind of supervised learning model that uses association rules to assign a target value. The term associative classification was coined by Bing Liu et al., [1] in which the authors defined a model made of rules "whose right-hand side are restricted to the classification class attribute".
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).
Classification of machine learning models can be validated by accuracy estimation techniques like the holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set.
Probabilistic classification models go beyond providing binary outputs and instead produce probability scores for each class. These models are designed to assess the likelihood or probability of an instance belonging to different classes.
Another aspect to consider for a model is what needs to be understood or predicted about that research area along with setting up any parameters to define the research. Another aspect of building a water quality model is knowing the audience and the exact purpose for presenting data like to enhance water quality management [7] for water quality ...
In 2018, Banerjee et al. [9] proposed a deep learning model for estimating short-term life expectancy (>3 months) of the patients by analyzing free-text clinical notes in the electronic medical record, while maintaining the temporal visit sequence. The model was trained on a large dataset (10,293 patients) and validated on a separated dataset ...
Linear classifier – Statistical classification in machine learning Fisher's linear discriminant – Method used in statistics, pattern recognition, and other fields; Logistic regression – Statistical model for a binary dependent variable
Rule-based machine learning (RBML) is a term in computer science intended to encompass any machine learning method that identifies, learns, or evolves 'rules' to store, manipulate or apply. [ 1 ] [ 2 ] [ 3 ] The defining characteristic of a rule-based machine learner is the identification and utilization of a set of relational rules that ...