Supervised

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In supervised learning, you provide a training data set with answers, such as a set of pictures of animals along with the names of the animals. The goal of that training would be a model that could correctly identify a picture (of a kind of animal that was included in the training set) that it had not previously seen. Training and evaluation turn supervised learning algorithms into models by optimizing their parameters to find the set of values that best matches the ground truth of your data. The algorithms often rely on variants of steepest descent for their optimizers, for example stochastic gradient descent (SGD), which is essentially steepest descent performed multiple times from randomized starting points. Common refinements on SGD add factors that correct the direction of the gradient based on momentum or adjust the learning rate based on progress from one pass through the data (called an epoch) to the next. Machine learning algorithms explained | Martin Heller - InfoWorld

This kind of learning is possible when inputs and the outputs are clearly identified, and algorithms are trained using labeled examples. To understand this better, let’s consider the following example: an equipment could have data points labeled F (failed) or R (runs). Machine Learning: What it is and Why it Matters | Priyadharshini @ simplilearn

There are two main types of supervised learning problems: they are classification that involves predicting a class label and regression that involves predicting a numerical value.

  • Classification: Supervised learning problem that involves predicting a class label.
  • Regression: Supervised learning problem that involves predicting a numerical label.

Both classification and regression problems may have one or more input variables and input variables may be any data type, such as numerical or categorical. 14 Different Types of Learning in Machine Learning | Jason Brownlee - Machine Learning Mastery

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