Supervised learning is machine learning algorithm start with a data set containing training examples with associated correct labels.In supervised machine learning, algorithmic models learn from labeled data (that is, data for which you know the outcome variable, or target) and make predictions for that outcome on new sets of data.Supervised machine learning systems provide the learning algorithms with known quantities to support future judgments. Chatbots, self-driving cars, facial recognition programs, expert systems and robots are among the systems that may use either supervised or unsupervised learning.
Supervised learning systems are mostly associated with retrieval-based AI but they may also be capable of using a generative learning model. For example, when learning to classify handwritten digits, a supervised learning algorithm takes thousands of pictures of handwritten digits along with labels containing the correct number each image represents. The algorithm will then learn the relationship between the images and their associated numbers, and apply that learned relationship to classify completely new images (without labels) that the machine hasn’t seen before. In another example suppose you want to predict whether someone will have a heart attack within a year. You have a set of data on previous patients, including age, weight, height, blood pressure, etc. You know whether the previous patients had heart attacks within a year of their measurements. So, the problem is combining all the existing data into a model that can predict whether a new person will have a heart attack within a year.
Importance of Supervised Machine Learning
Supervised machine learning provides a direct path for turning data into real, actionable insights. Using data as a resource, it enables organizations to understand and prevent unwanted outcomes or boost desired outcomes for whatever it is they’re trying to predict.supervised machine learning has historically required extensive amounts of time and technical expertise from a team of data scientists in order to build, scale, and deploy accurate predictive models. Additionally, because supervised learning models make real-world predictions based on past data, models must be periodically rebuilt in order to keep their predictions from becoming obsolete as the world (and by proxy, the data) changes.
Steps in Supervised Learning
The steps for supervised learning are
1. Prepare Data
2. Choose an Algorithm
3. Fit a Model
4. Choose a Validation Method
5. Examine Fit and Update Until Satisfied
6. Use Fitted Model for Predictions
Task of Supervised Learning
Classification is a technique by which you determine to what group a certain observation belongs; for example, biologists categorizing plants, animals, and other lifeforms into different taxonomies is a form of classification. One of the primary uses of data science and machine learning is to predict a category to which an observation most likely belongs.In classification, the goal is to assign a class (or label) from a finite set of classes to an observation. That is, responses are categorical variables. Applications include spam filters, advertisement recommendation systems, and image and speech recognition. Predicting whether a patient will have a heart attack within a year is a classification problem, and the possible classes are true and false. Classification algorithms usually apply to nominal response values.
Regression, one of the most common machine learning models, estimates the relationships between variables. In short, regression is everything classification isn’t. Classification predicts which category an observation belongs to, such as gender, while regression estimates a numeric value, such as height.In regression, the goal is to predict a continuous measurement for an observation. That is, the responses variables are real numbers. Applications include forecasting stock prices, energy consumption, or disease incidence. It allows you to estimate a value, such as housing prices or human lifespan, based on input data