default from paying loans or credit card bills This is of paramount importance

# Default from paying loans or credit card bills this

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default from paying loans or credit card bills. This is of paramount importance as machine learning would help the banks to identify the customers who can be granted loans and credit cards. Healthcare : It is used to diagnose deadly diseases (e.g. cancer) based on the symptoms of patients and tallying them with the past data of similar kind of patients. Retail : It is used to identify products which sell more frequently (fast moving) and the slow moving products which help the retailers to decide what kind of products to introduce or remove from the shelf. Also, machine learning algorithms can be used to find which two / three or more products sell together. This is done to design customer loyalty initiatives which in turn helps the retailers to develop and maintain loyal customers. Chatbots : ML based chatbots are used in various domains in B2B and B2C setting
From the perspective of inductive learning, we are given input samples (x) and output samples (f(x)) and the problem is to estimate the function (f). Specifically, the problem is to generalize from the samples and the mapping to be useful to estimate the output for new samples in the future. In practice it is almost always too hard to estimate the function, so we are looking for very good approximations of the function. Some practical examples of induction are: Credit risk assessment . The x is the properties of the customer. The f(x) is credit approved or not. Disease diagnosis . The x are the properties of the patient. The f(x) is the disease they suffer from. Face recognition . The x are bitmaps of peoples faces. The f(x) is to assign a name to the face. Automatic steering . The x are bitmap images from a camera in front of the car. The f(x) is the degree the steering wheel should be turned.
F measure Precision Recall Accuracy RMSE (Root Mean Square Error) MAE (mean Absolute Error)
RMSE (ranges from 0 to infinity, lower is better), also called Root Mean Square

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