Technology, Science and Universe
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- Supervised LearningDescription: Supervised learning is an approach within the field of machine learning where a model is trained using a labeled dataset. This(...) Read more
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- The Unsupervised LearningDescription: Unsupervised learning is a type of machine learning where the model is trained on unlabeled data. Unlike supervised learning, where(...) Read more
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- Data adjustmentDescription: Data adjustment is the process of modifying and preparing datasets with the aim of improving the performance of a machine learning(...) Read more
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- Input attributeDescription: An input attribute is a feature or variable used as input to a machine learning model. These attributes are fundamental to the(...) Read more
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- Output AttributeDescription: The 'Output Attribute' in the context of AutoML refers to the target variable or output that a machine learning model predicts.(...) Read more
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- Bias adjustmentDescription: Bias adjustment is a critical process in the field of machine learning and data preprocessing, aimed at correcting inherent(...) Read more
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- Algorithm accelerationDescription: Algorithm acceleration refers to the process of improving the execution speed of algorithms through various optimization(...) Read more
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- Error analysisDescription: Error analysis is the process of identifying and understanding the mistakes made by a model. This process is fundamental in the(...) Read more
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- Complexity adjustmentDescription: Complexity adjustment is a fundamental process in the field of machine learning (AutoML) that focuses on managing the complexity of(...) Read more
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- Data adjustmentsDescription: Data adjustment is a fundamental process in the field of machine learning that involves preparing and modifying data to optimize(...) Read more
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- Feature tuningDescription: Feature tuning in the context of AutoML refers to the process of optimizing and selecting the features or characteristics that will(...) Read more
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- Algorithmic Transparency Description: Algorithmic transparency refers to the degree to which the processes and decisions of an algorithm can be understood by humans.(...) Read more
- Antecedent Analysis Description: Antecedent Analysis in the context of Explainable Artificial Intelligence (XAI) refers to a systematic method for examining(...) Read more
- Adversarial Examples Description: Adversarial examples are inputs intentionally designed to deceive an artificial intelligence model, causing it to make errors in(...) Read more
- Abductive Reasoning Description: Abductive reasoning is a form of logical inference that seeks the simplest and most probable explanation for observations. Unlike(...) Read more
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