Overview#

Supervised Learning is learning from a Supervisor.

Supervised Learning exhibits the following:

• Classification
  • Logistic Regression
  • K-Nearest Neighbor (KNN)
  • Naive-Bayes
  • SVM
• Linear Regression

• Decision Tree
• Direct Feedback
• Predict outcomes

Supervised Learning will always have a Training dataset

Supervised Learning Machine Learning#

Supervised Learning is the Machine Learning objective to determine the Mapping function from training dataset.

* Classification

• Training dataset
• Mapping function
• Machine Learning model
• Predictor variables

In Supervised Learning, each example in the Supervised Learning is a pair consisting of an input object (typically a vector) and an output value (called the supervisory signal).

A Supervised Learning algorithm analyzes the training dataset and produces a Mapping function, which can be used for mapping new data. An optimal scenario will allow for the Mapping function to correctly determine the classification for unseen data. This requires the Mapping function to generalize from the training dataset to unseen situations in a "reasonable" way (see Bias error).

Supervised Learnings may be either a: (Classification and Regression Trees)

• Classification
• Regression

More Information#

There might be more information for this subject on one of the following:

• Artificial Neural network
• Backpropagation
• Classification
• Classification Trees
• Deep Learning
• K-Nearest Neighbor
• Learning
• Logistic Regression
• Machine Learning
• Machine Learning Algorithms
• Machine Learning model
• Reinforcement learning
• Training dataset
• Unsupervised Learning

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• [#1] - Supervised_learning - based on information obtained 2017-11-24-