supervised learning -지도학습

출처: machine learning by andrew ng, stanford cousera lecture

수강일시: 2021.01.24 

 

 

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machine learning - Andrew Ng, Stanford(coursera)

let's think of an example of sizeof house and housing price

we have data of former price and size of house and now we want to find the price of new house.

 

The term Supervised Learning refers to 

the fact that we gave the algorithm a data set in which the, called, "right answers" were given. 

That is we gave it a data set of houses in which for every example in this data set, 

we told it what is the right price.

 

 

also it could be said as regression problem.

 

another case is about breast cancer.

if we know the size of tumor, can we estimate the probability of that tumor being malignant?

this is problem of classification.

 

most real word ml problem consider not only 2 or3 but thousands of features

 

-> using svm(support vector machine) make us allow to use infinit number of features

 

 

 

 

Q

 

You’re running a company, and you want to develop learning algorithms to address each of two problems. Problem 1:You have a large inventory of identical items. You want to predict how many of these items will sell over the next 3 months.

Problem 2: You’d like software to examine individual customer accounts, and for each account decide if it has been hacked/compromised. Should you treat these as classification or as regression problems?

 

1.Treat both as classification problems.

2.Treat problem 1 as a classification problem, problem 2 as a regression problem.

3.Treat problem 1 as a regression problem, problem 2 as a classification problem.

4.Treat both as regression problems.

 

answer 3