Classification of Machine Learning-Part 1: Supervised Learning
There are three main types of Machine learning: Supervised, Unsupervised and Reinforcement Learning. This post is the first of our serie of three posts on classification of Machine Learning. Supervised machine learning is a way to create Artificial Intelligence (AI). A computer algorithm is proficient in labeled input data for a specific output. The computer algorithm is trained in detecting the underlying relationships and patterns existing between input data and output labels, allowing it to produce precise labeled results when presented with unforeseen data. IBM has spent years developing these supervised learning models to enable organizations to create highly accessible machine learning models.
Supervised learning is also built on training like other machine learning algorithms. The labeled data sets are added in the system, which teaches the system-specific output relevant to each input value. After training, the model is given test data; the data is already labeled without revealing the labels to the algorithm. The purpose of this test data is to measure the accuracy of the output produced by the algorithm.
The types of Supervised Learning Algorithms are Classification and Regression. Classification utilizes an algorithm for assigning the test data into particular categories in an accurate way. It identifies particular units in the data and tries to conclude the labels for the data set. The most common classification algorithms include Support Vector Machines (SVM), Linear classifiers, Random forest, K-nearest neighbor, and Decision trees. Regression comprehends the association between independent and dependent variables and is usually used for making predictions for sales revenue for a specific business organization. The popular regression algorithms include Linear regression, Polynomial regression, and Logistical regression. The algorithms are divided into various categories depending on their functions.
Example of supervised learning may include training a machine for predicting the time duration from home to the workplace. The labeled data will be created for input in this example. The data will include weather conditions, holidays, time of the day, etc. These details are the input, and the output will include the time it will take to drive from home to work on that specific day.
The different models of supervised learning may be utilized for developing various applications for business purposes. This may include using supervised machine learning algorithms to locate, isolate, and categorize objects from images or videos and use them for different computer vision or image analysis techniques. This can also be used to develop predictive analytics, customer sentiment analysis, spam detection, and several other purposes.
Concluding Words
The supervised learning machine helps in collecting or producing data output from prior experiences. It can help in optimizing performance criteria through experience and also helps in solving different reality-based computational problems. Supervised learning can prove quite beneficial for businesses. However, there exist some challenges while developing sustainable models for supervised learning. Some of the challenges that may occur include, accuracy may be influenced when irrelevant and incomplete values have been used as input data. The prior processing and preparation of data are challenging. They can be very time-consuming, and datasets have an increased chance of human error, leading to incorrect output as they can not classify data independently. Hence, the supervised models may prove to be an efficient solution in eradicating manual classification and producing predictions for the future, depending on the labeled data. The machine needs human knowledge and expertise for training and producing accurate output data.
References
Kotsiantis, S. B., Zaharakis, I., & Pintelas, P. (2007). Supervised machine learning: A review of classification techniques. Emerging artificial intelligence applications in computer engineering, 160(1), 3–24.
Singh, A., Thakur, N., & Sharma, A. (2016, March). A review of supervised machine learning algorithms. In 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom) (pp. 1310–1315). Ieee
https://www.ibm.com/cloud/learn/supervised-learning
https://searchenterpriseai.techtarget.com/definition/supervised-learning
