Supervised learning is a type of learning method in Machine Learning or Artificial Intelligence. In supervised learning, we use labeled datasets to train algorithms that to classify data or predict outcomes accurately. As we feed labelled input data into the model, it adjusts its weights and biases iteratively, which ensures that the model has been fitted appropriately.
Supervised learning is used in organisations to solve a variety of real-world problems at scale, such as classifying spam in a separate folder from your inbox.
Basic working of Supervised Learning
Supervised learning employs a training set to teach models how to spit out the desired output. The training dataset consists of inputs and correct outputs, which allows the model to learn better over a period of time. Accuracy is measured using the loss function, and we keep on adjusting the weights until the error is minimum.
Supervised learning can be separated into two types of problems :
- Classification, which clearly classifies the data into categories. An algorithm is implemented to accurately assign test data into specific categories. It recognizes specific entities within the dataset and attempts to draw some conclusions on how those entities should be labeled or defined. Common classification algorithms are linear classifiers, support vector machines (SVM), decision trees, k-nearest neighbor, and random forest.
- Regression is used to understand the relationship between dependent and independent variables. It is commonly used to make projections, such as for sales revenue for a given business. Linear regression, logistical regression, and polynomial regression are popular regression algorithms.
Difference between Supervised Learning and Unsupervised Learning
Difference between supervised and unsupervised learning in details: https://www.javatpoint.com/difference-between-supervised-and-unsupervised-learning
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