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Over the past two decades, Machine Learning algorithms are used in many areas like the Banking sector, online sites, social network sites, Health sector etc. By applying Machine learning algorithms on past data of the organization, one can take certain decision for the benefits of organization. This paper mainly focuses on explaining the concept and applying different Machine Learning algorithms in R programming over different datasets.
Machine learning enables analysis of massive quantities of data. While it generally delivers faster, more accurate results in order to identify profitable opportunities or dangerous risks for organizations, it may also require additional time and resources to train it properly.
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.
The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers to learn automatically without human intervention or assistance and adjust actions accordingly.
Machine learning algorithms are often categorized as supervised or unsupervised.
Supervised machine learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events. Starting from the analysis of a known training dataset, the learning algorithm produces an inferred function to make predictions about the output values. The system is able to provide targets for any new input after sufficient training. The learning algorithm can also compare its output with the correct, intended output and find errors in order to modify the model accordingly.
In contrast, unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data. The system doesn’t figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data. Semi-supervised machine learning algorithms fall somewhere in between supervised and unsupervised learning since they use both labeled and unlabeled data for training – typically a small amount of labeled data and a large amount of unlabeled data. The systems that use this method are able to considerably improve learning accuracy. Usually, semi-supervised learning is chosen when the acquired labeled data requires skilled and relevant resources in order to train it / learn from it.
Otherwise, acquiring unlabeled data generally doesn’t require additional resources. Reinforcement machine learning algorithms is a learning method that interacts with its environment by producing actions and discovers errors or rewards. Trial and error search and delayed reward are the most relevant characteristics of reinforcement learning. This method allows machines and software agents to automatically determine the ideal behavior within a specific context in order to maximize its performance. Simple reward feedback is required for the agent to learn which action is best; this is known as the reinforcement signal.
Classification and regression come under supervised learning algorithms whereas clustering algorithm comes under unsupervised learning algorithms.
It is an approach for modeling relationship between scalar dependent variable x and one or more explanatory variables x. one explanatory variable means simple linear regression. More than one explanatory variable mean multiple linear regression.
Let us consider the following data sets which contain size and price of houses, here size is explanatory variable whereas y is the dependent variable.
After applying linear regression to above dataset in R programming the following graph is generated in which red line represents model i.e., y=a+bx Where c is constant and b is a slope. With help of above graph now we can predict the future. For example in R – Programming there is a function predict() used to predict the future price by giving size as follows.
For the input house of size is 1600 linear regression model predicting the value as 231.523.
Logistic Regression is a classification, not a regression algorithm. It is used to estimate discrete values ( Binary values like 0/1, yes/no, true/false ) based on given set of independent variable(s). In simple words, it predicts the probability of occurrence of an event by fitting data to a logit function. Hence, it is also known as logit regression. Since it predicts the probability, its output values lie between 0 and 1 (as expected).
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