3 Basic Types Of Machine Learning Problems
The term “machine learning” is often mistakenly confused with artificial intelligence [JB1], but machine learning is actually a sub-term.
Field type/AI. Machine learning is also often referred to as predictive analytics or predictive modeling.
The term “machine learning” was coined by American computer scientist Arthur Samuel in 1959 and is defined as “the computer’s ability to learn without being explicitly programmed”.
In its most basic form, machine learning uses programmed algorithms that receive and analyze input data to predict output values within an acceptable range. As these algorithms are fed new data, they learn and modify their operations to improve performance, developing “intelligence” over time.
There are four types of machine learning algorithms: supervised, semi-supervised, unsupervised, and enhanced.
Read More: What Are The 3 Basic Types Of Machine Learning Problems?
Supervised teaching In supervised learning, the machine is taught using examples. The operator provides the machine learning algorithm with a known set of data that contains the desired inputs and outputs, and the algorithm must find a way to figure out how to get those inputs and outputs. While the operator knows the correct answers to the problem, the algorithm identifies patterns in the data, learns from observations, and makes predictions. The algorithm makes predictions and the operator corrects them. This process continues until the algorithm reaches a high level of accuracy/performance.
Under the Decline of Supervised Learning: Classification, Regression, and Prediction.
Classification: In classification tasks, the machine learning program must draw and determine a conclusion from the observed values which category the new observations belong to. For example, if the emails are filtered as “spam” or “not spam”, the program should analyze the existing observation data and filter the emails accordingly.
Regression — In regression tasks, the machine learning program must estimate and understand the relationships between variables. Regression analysis focuses on one dependent variable and a number of other changing variables, which makes it particularly useful for predictions and forecasting.
Forecasting — Forecasting is the process of making predictions about the future based on past and current data and is often used to analyze trends.
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