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Training: Difference between revisions
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==Purpose of Training== | ==Purpose of Training== | ||
Training involves discovering patterns and relationships amongst the data which can be used for making predictions about unseen data. Training allows the | Training involves discovering patterns and relationships amongst the data which can be used for making predictions about unseen data. Training allows the model to generalize from what it has seen so that it can accurately make predictions even when presented with unknown variables. | ||
==Types of Training== | ==Types of Training== | ||
Machine learning employs various types of training, such as supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning. | Machine learning employs various types of training, such as [[supervised learning]], [[unsupervised learning]], [[semi-supervised learning]] and [[reinforcement learning]]. | ||
===Supervised Learning=== | ===Supervised Learning=== | ||
Supervised learning is the most common type of machine learning training. This method involves providing the algorithm with a labeled dataset, in which each data point has an assigned class label. The algorithm then utilizes this labeled data to learn relationships between features in the data and their corresponding labels. Ultimately, supervised learning aims to build a model capable of accurately predicting labels for new, unseen data sets. | [[Supervised learning]] is the most common type of machine learning training. This method involves providing the algorithm with a [[labeled dataset]], in which each [[data point]] has an assigned [[class]] [[label]]. The algorithm then utilizes this labeled data to learn relationships between [[features]] in the data and their corresponding labels. Ultimately, supervised learning aims to build a model capable of accurately predicting labels for new, unseen [[data sets]]. | ||
===Unsupervised Learning=== | ===Unsupervised Learning=== | ||