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Predictive learning is a machine learning (ML) technique where an artificial intelligence model is fed new data to develop an understanding of its environment, capabilities, and limitations. This technique finds application in many areas, including neuroscience , business , robotics , and computer vision .
Ensemble learning, including both regression and classification tasks, can be explained using a geometric framework. [15] Within this framework, the output of each individual classifier or regressor for the entire dataset can be viewed as a point in a multi-dimensional space.
RULES-1 [3] is the first version in RULES family and was proposed by prof. Pham and prof. Aksoy in 1995. RULES-2 [4] is an upgraded version of RULES-1, in which every example is studied separately. RULES-3 [5] is another version that contained all the properties of RULES-2 as well as other additional features to generates more general rules.
A simple review of the above table should make these rules obvious. The support for Rule 1 is 3/7 because that is the number of items in the dataset in which the antecedent is A and the consequent 0. The support for Rule 2 is 2/7 because two of the seven records meet the antecedent of B and the consequent of 1. The supports can be written as:
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 ...
Predictive analytics statistical techniques include data modeling, machine learning, AI, deep learning algorithms and data mining. Often the unknown event of interest is in the future, but predictive analytics can be applied to any type of unknown whether it be in the past, present or future.
One way of resolving the trade-off is to use mixture models and ensemble learning. [ 14 ] [ 15 ] For example, boosting combines many "weak" (high bias) models in an ensemble that has lower bias than the individual models, while bagging combines "strong" learners in a way that reduces their variance.
Formally, an "ordinary" classifier is some rule, or function, that assigns to a sample x a class label ลท: y ^ = f ( x ) {\displaystyle {\hat {y}}=f(x)} The samples come from some set X (e.g., the set of all documents , or the set of all images ), while the class labels form a finite set Y defined prior to training.