What is supervised machine learning?
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Supervised machine learning is a branch of machine learning where algorithms are trained using labelled datasets to make predictions or classifications . In supervised learning, the training data acts as a supervisor, guiding the machine to predict correct outputs. Common algorithms used in supervised learning include the K-Nearest Neighbor (KNN) method, which calculates distances to classify objects, such as predicting diabetes in women with high accuracy rates. This approach allows organizations to tackle real-world challenges like spam classification effectively. Supervised learning contrasts with unsupervised learning, where algorithms work without guidance, showcasing the importance of labelled data in training models for accurate predictions.
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| Papers (5) | Insight |
|---|---|
| Supervised machine learning is a method where models are trained using labeled data to make predictions or classifications, such as the K-Nearest Neighbor (KNN) method used in predicting diabetes in women. | |
| Supervised machine learning involves training with labeled data to predict outputs. The training data guides the machine to learn correct outputs, as discussed in Statistical Decision Theory and other methods. | |
| Supervised machine learning is a type of machine learning where algorithms are trained using labeled data to make predictions or decisions based on input variables. | |
| Supervised machine learning is a subfield of machine learning and artificial intelligence that involves training classification or prediction algorithms using labelled datasets. | |
| Supervised learning is a branch of machine learning where the machine uses labeled data to create models that can predict labels for unlabeled data. |
Related Questions
What is supervised feature selection?5 answersSupervised feature selection is a technique used in data mining and machine learning to select a subset of informative features from a larger set of available features. It aims to enhance model interpretability and performance while reducing resource demands. There are several methods proposed in the literature for supervised feature selection. One approach combines linear discriminant analysis (LDA) with l2,p-norm regularization and imposes an orthogonal constraint on the projection matrix. Another method uses sparse neural networks to efficiently prune uninformative features from the input layer. Additionally, a collaborative neurodynamic optimization (CNO) approach has been proposed, which formulates feature selection as a mixed-integer optimization problem and uses recurrent neural networks or projection networks to solve it. These methods have been evaluated on various datasets and have shown promising results in terms of classification accuracy and computational efficiency.
What is supervised machine learning models?4 answersSupervised machine learning models are a subfield of machine learning and artificial intelligence that involve training classification or prediction algorithms using labeled datasets. These models adjust their weights through cross-validation to fit the data well. They are used to find solutions to real-world challenges such as spam classification and removal from inboxes. In supervised classification, systems reason from given instances to generate hypotheses and predictions for future instances. Different supervised learning algorithms are compared based on factors like precision, accuracy, and minimal error. Machine learning algorithms, including supervised learning, automatically learn from experience and improve without explicit programming. They provide good accuracy and have applications in various fields. Supervised machine learning models have also been used to determine early-stage symptoms of diseases like COVID-19.
Supervised machine learning?4 answersSupervised machine learning is a powerful approach that can be used in various fields, including medicine and psychology. In the medical field, supervised machine learning algorithms have been applied to predict heart disease with high accuracy, achieving up to 94.1% accuracy and demonstrating potential for practical application. In the field of psychology, supervised machine learning has been used to predict psychological outcomes, such as Big Five personality trait scores, using nonlinear random forest models. Additionally, supervised machine learning has been utilized to infer important properties of species trees and predict gene tree discordance in genomic datasets. These examples highlight the versatility and effectiveness of supervised machine learning in different domains.
What is supervised learning?4 answersSupervised learning is a technique in machine learning where algorithms are trained using labelled datasets to make predictions or classifications. It involves mapping input data to corresponding output data based on the provided labels. This type of learning is characterized by the presence of a supervisor or training data that guides the machine in predicting the correct output. Supervised learning can be used for various real-world challenges such as spam classification and disease prediction.
What is supervised learnunf?4 answersSupervised learning is a machine learning technique that uses labeled training data to extract general principles and make predictions based on observed examples. It has been applied in various fields, including genetics, composite materials, collaborative learning, and time series classification. In genetics, supervised learning has been used to predict gene attributes by leveraging molecular interaction networks. In the field of composite materials, supervised learning models have been trained to predict mechanical properties with reasonable accuracy and generalizability. In collaborative learning, the Assisted Learning framework has been introduced, where a service provider assists a user with supervised learning tasks without sharing private algorithms or data. In time series classification, a semi-supervised model has been proposed that leverages features learned from self-supervised tasks on unlabeled data, outperforming state-of-the-art baselines.
What is SEMI-SUPERVISED LEARNING?4 answersSemi-supervised learning is a technique that combines labeled data with a larger amount of unlabeled data to improve learning performance. It is particularly useful in scenarios where obtaining a fully labeled dataset is challenging or costly. Several papers in the provided abstracts discuss different approaches to semi-supervised learning. Lu and Wu propose a paradigm called co-training-teaching (CoT2) that integrates co-training and co-teaching to improve the robustness of semi-supervised learning in review-aware rating regression (RaRR). Purpura-Pontoniere et al. present Semi-Supervised Relational Contrastive Learning (SRCL), a model that leverages self-supervised contrastive loss and sample relation consistency for effective exploitation of unlabeled data in disease diagnosis from medical images. Wang et al. propose a similarity graph structure learning (SGSL) model and an uncertainty-based graph convolutional network (UGCN) to reduce noise in pseudo-labels and improve the performance of semi-supervised learning. Li et al. propose a semi-supervised learning method based on the Diff-CoGAN framework for medical image segmentation, which incorporates co-training and generative adversarial network (GAN) strategies.