Supervised Learning for Machine Learning

Depending on the data type, the modeling method for a problem is different. In the field of machine learning or artificial intelligence, people will first consider algorithm learning methods. There are several main learning methods in the machine learning field. Classifying algorithms by learning is a good idea. This allows people to consider selecting the most appropriate algorithm based on input data during modeling and algorithm selection to obtain the best results.

Under supervised learning, input data is called "training data". Each group of training data has a clear identifier or result, for example, for "spam" and "non-spam" in the Anti-Spam system, for "1", "2", "3", "4" in handwritten numbers, etc. When establishing a prediction model, supervised learning establishes a learning process, compares the prediction results with the actual results of "training data", and constantly adjusts the prediction model, until the prediction result of the model reaches an expected accuracy. Common scenarios of supervised learning, such as classification and regression. Common algorithms include Logistic regression and Back Propagation Neural Network ).

Supervised Learning for Machine Learning