Comments about "Machine_learning" in Wikipedia

This document contains comments about the article Machine_learning in Wikipedia
In the last paragraph I explain my own opinion.

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    The article starts with the following sentence.

    1. Overview

    2. History and relationships to other fields

    2.1 Artificial intelligence

    2.2 Data mining

    2.3 Optimization

    2.4 Generalization

    2.5 Statistics

    2.6 Physics



    4.1 Supervised learning

    4.2 Unsupervised learning

    4.3 Semi-supervised learning

    4.4 Reinforcement learning

    4.5 Dimensionality reduction

    4.6 Other types

    4.6.1 Self-learning

    4.6.2 Feature learning

    4.6.3 Sparse dictionary learning

    4.6.4 Anomaly detection

    4.6.5 Robot learning

    4.6.6 Association rules

    4.7 Models

    4.7.1 Artificial neural networks

    4.7.2 Decision trees

    4.7.3 Support-vector machines

    4.7.4 Regression analysis

    4.7.5 Bayesian networks

    4.7.6 Gaussian processes

    4.7.7 Genetic algorithms

    4.8 Training models

    4.8.1 Federated learning

    5. Applications


    Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results.
    Always what you expect that a machine should learn, a human should also be able to perform.
    For example: if a doctor can not heal a patient, you should not be sure that a machine-learning program can

    6.1 Bias

    6.2 Explainability

    Explainable AI (XAI), or Interpretable AI, or Explainable Machine Learning (XML), is artificial intelligence (AI) in which humans can understand the decisions or predictions made by the AI.
    But they must also be the best. That means they should be agree with a human test case, using the same data base.
    It contrasts with the "black box" concept in machine learning where even its designers cannot explain why an AI arrived at a specific decision.
    Such an answer is considered wrong. The system should also explain why it comes to such a 'wrong' conclusion. . If required should show all the intermediate steps.
    XAI may be an implementation of the social right to explanation.
    AI should always have a mode that it shows all the intermediate steps

    6.3 Overfitting

    Settling on a bad, overly complex theory gerrymandered to fit all the past training data is known as overfitting.
    This sentence is not clear.

    6.4 Other limitations and vulnerabilities

    7.Model assessments



    9.1 Neuromorphic/Physical Neural Networks

    9.2 Embedded Machine Learning


    10.1 Free and open-source software

    10.2 Proprietary software with free and open-source editions

    10.3 Proprietary software



    13. See also

    Following is a list with "Comments in Wikipedia" about related subjects

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    Created: 11 January 2022

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