What is the process for Supervised learning ML?
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Supervised learning in machine learning involves training models using labelled datasets to predict outputs accurately. The process begins with providing well-labelled training data, where the correct output is attached to corresponding input data . The model's weights are adjusted through cross-validation until the data fits well, optimizing class label models using predictor features . This training phase is followed by testing the model with unseen data to classify or approximate outputs, deploying algorithms like XGBoost, Naïve Bayes, Support Vector Machine, and others . Supervised learning encompasses both classification and regression tasks, where regression is used for continuous outputs and classification for categorical outputs . Overall, supervised learning aims to enable machines to learn from data without human intervention, offering solutions to various real-world challenges like spam classification and removal .
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| Papers (4) | Insight |
|---|---|
| Supervised learning in ML involves training with labelled data to predict outputs. It utilizes Statistical Decision Theory, Gaussian Distribution, Binary Components, Beliefs Network, and Nearest-Neighbour Methods for training. | |
| Supervised learning in ML involves training algorithms with labelled datasets, adjusting weights through cross validation until a good fit is achieved, enabling solutions for real-world challenges like spam removal. | |
04 Apr 2022 3 Citations | Supervised learning in ML involves pre-training on labeled data for classification or regression tasks, optimizing models using predictor features, and assigning class labels to test data based on known characteristics. |
| Supervised learning in ML involves finding relationships between data pairs through methods like linear regression, classification using various techniques such as logistic regression, SVM, and neural networks with back-propagation. |
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