Following is listed different categories which covers 80% of machine learning problems:
Simply speaking, if the answer to problems consists of discrete values such as some of the following, the problem can be termed as classification problems. These are called as “Logistic Regression” problems.
Mathematically speaking, if “h(x)” is a hypothetical function, the value of h(x) would fall within 0 and 1. h(x) could be read as estimated probability that output is 0 or 1 on input x. In order to predict whether the output is either 1 (yes or positive class) or 0 (no or negative class), we may compare the value of h(x) with 0.5. It the value is greater than 0.5, we may predict the output as 1 (yes) or otherwise 0 (no). Well, if you are aware of naive Bayes, you may be smiling by now. This is because the above definition of h(x) could be written as probability that y=0 or 1 is true given x has occurred. Following are some of the examples:
Following are some of the algorithms which could be used to solve classification problems:
Clustering problems are about grouping similar things together. Take a look at some of the following example:
Regression problems are more related with predicting numbers based on input data sets. Mathematically, following is the formulae to solve regression problems:
# Linear regression; h(x) is hypothesis function, m is parameter, x is feature (or variable) and c is constant.
h(x) = mx + c
# Multiple regression; m1, m2, m3, .... mn are parameters and x1, x2, x3...xn are features
h(x) = c + m1.x1 + m2.x2 + m3.x3 + ... + mn.xn
Following are some of the examples:
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