Machine Learning algorithms is provided with Input as well as Output pairs during the learning phase?
Answers
Answer:
Machine learning encompasses a vast set of ideas, tools, and techniques with which Data Scientists and other professionals use.
Supervised Machine Learning Problems and Solutions
The most straightforward tasks fall under the umbrella of supervised learning. In supervised learning, we have access to examples of correct input-output pairs that we can show to the machine during the training phase. The common example of handwriting recognition is typically approached as a supervised learning task. We show the computer a number of images of handwritten digits along with the correct labels for those digits, and the computer learns the patterns that relate images to their labels.
Learning how to perform tasks in this way, by explicit example, is relatively easy to understand and straightforward to implement, but there is a crucial task: we can only do it if we have access to a dataset of correct input-output pairs. In the handwriting example, this means that at some point we need to send a human in to classify the images in the training set. This is laborious work and often infeasible, but where the data does exist, supervised learning algorithms can be extremely effective at a broad range of tasks.
Supervised machine learning tasks can be broadly classified into two subgroups: regression and classification. Regression is the problem of estimating or predicting a continuous quantity. What will be the value of the S&P 500 one month from today? How tall will a child be as an adult? How many of our customers will leave for a competitor this year? These are examples of questions that would fall under the umbrella of regression. To solve these problems in a supervised machine learning framework, we would gather past examples of “right answer” input/output pairs that deal with the same problem. For the inputs, we would identify featuresthat we believe would be predictive of the outcomes that we wish to predict.