|Type of paper:||Course work|
|Categories:||Learning Artificial intelligence|
Various machine learning approaches are employed by data scientists to discover the data patterns that can offer actionable insights in big data. These algorithms are classified into supervised and unsupervised depending on the way in which they deduce data to come up with predictions. The supervised learning puts in place a training data that has a label. Labeled data is the data with a clearly set output value. The supervised learning solves regression and classification problems. On the other hand, unsupervised learning does not utilize a training set of data and find patterns or structure in the available data by themselves. The unsupervised learning technique solves clustering problems (Alpaydine, 2014).
Supervised machine learning derives its name from the fact that a data scientist guides it thus teaching the algorithm the conclusion it is expected to bring forth. The use of supervised learning is similar to the child learning the alphabet from a teacher. The supervised learning requires the possible output from the algorithm to be known already. The data used in training the algorithm is known and labeled with correct answers (Buttou, 2014). For instance, a classification algorithm can learn to identify various animals after a training based on a data set of properly labeled images of the animal species as well as the identifying characteristics.
Principles of Supervised learning
The machine learning process is based on the software system concept that improves its response by means of learning. The improvement is through new data acceptance and the resulting behavior. Genetic algorithms and neutral works are similar to the machine learning process. The genetic algorithm utilizes the fitness function to select an algorithm with the best score in line with the fitness function. The Iteration and feeding of the framework determine the output (Alpydine, 2014).
Application of Supervised learning
Companies use supervised data in front-end presentation and background data analysis (Alpydine, 2014). For instance, the recommendations at companies such as Facebook, Netflix, Youtube, and Amazon contain Image classification, face recognition, and speech recognition exemplifies the outcome of supervised learning. The same process is now applicable to the concepts behind self-driving cars.
Challenges and Limitations of Supervised Learning
Since the supervised learning undertakes various tasks based on predetermined rules that the user sets in place, the user cannot be sure of the labels that can pre-define the rules in a case where the data is dynamic growing.
Supervised learning cannot classify or cluster data through the discovery of their features by itself. The supervised learning learns how to map the input data set towards the output data set. This way it predicts the output of an input that is unseen. Since it needs known input data and output data set for the training to take place, it does not classify or cluster based on the features of the set data (Guyon et al, 2012).
The communication prior to data engineering is essential in solving any underlying problem. When the question is not specific and well labeled will constitute a challenge in the learning process. In terms of scalability, the supervised learning is limited depending on the target function at hand (Guyon et al, 2012). The input data determine the output and since the input data is labeled and destined for a specified output, no new information can be obtained in the process.
Alpaydin, E. (2014). Introduction to machine learning. MIT press.Bottou, L. (2014). From machine learning to machine reasoning. Machine learning, 94 (2), 133-149.
Guyon, I. et al. (2012). Active Learning Challenge: challenges in Machine Learning. Massachusetts; Microtome Publishing.
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