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lpounng
lpounng
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The reason is an unsupervised algorithm is used for a supervised problem.

Remember there is no strict right or wrong in unsupervised learning. For example, say I have a dataset of 3 attritutes, "Age", "Blood Pressure" and "Income". Both "Blood Pressure" and "Income" consists of 2 categories, "high" and "low".

There are 2 things I can do:

  1. I can set "Income" as target, and train a supervised model to predict it from "Age" and "Blood Pressure". (of course Blood Pressure may have no predictive power at all)

  2. I can also feed the 2 attributes "Age" and "Blood Pressure" into an unsupervised algorithm e.g. KMeans, and ask it to return 2 groups.

From 2., there is a chance that the algorithm gives back 2 groups, which turns out to be the two Blood Pressure clusters. Is this grouping useful to predict "Income"? Probably not. But it is not wrong either - it correctly identified 2 Blood Pressure groups. Just not related to income.

So in your case, the algorithm has detected 2 clusters, but not necessary related to DEATH_EVENT. Supervised algorithms should be used if you trywant to predict thingsmake predictions.

The reason is an unsupervised algorithm is used for a supervised problem.

Remember there is no strict right or wrong in unsupervised learning. For example, say I have a dataset of 3 attritutes, "Age", "Blood Pressure" and "Income". Both "Blood Pressure" and "Income" consists of 2 categories, "high" and "low".

There are 2 things I can do:

  1. I can set "Income" as target, and train a supervised model to predict it from "Age" and "Blood Pressure". (of course Blood Pressure may have no predictive power at all)

  2. I can also feed the 2 attributes "Age" and "Blood Pressure" into an unsupervised algorithm e.g. KMeans, and ask it to return 2 groups.

From 2., there is a chance that the algorithm gives back 2 groups, which turns out to be the two Blood Pressure clusters. Is this grouping useful to predict "Income"? Probably not. But it is not wrong either - it correctly identified 2 Blood Pressure groups. Just not related to income.

So in your case, the algorithm has detected 2 clusters, but not necessary related to DEATH_EVENT. Supervised algorithms should be used if you try to predict things.

The reason is an unsupervised algorithm is used for a supervised problem.

Remember there is no strict right or wrong in unsupervised learning. For example, say I have a dataset of 3 attritutes, "Age", "Blood Pressure" and "Income". Both "Blood Pressure" and "Income" consists of 2 categories, "high" and "low".

There are 2 things I can do:

  1. I can set "Income" as target, and train a supervised model to predict it from "Age" and "Blood Pressure". (of course Blood Pressure may have no predictive power at all)

  2. I can also feed the 2 attributes "Age" and "Blood Pressure" into an unsupervised algorithm e.g. KMeans, and ask it to return 2 groups.

From 2., there is a chance that the algorithm gives back 2 groups, which turns out to be the two Blood Pressure clusters. Is this grouping useful to predict "Income"? Probably not. But it is not wrong either - it correctly identified 2 Blood Pressure groups. Just not related to income.

So in your case, the algorithm has detected 2 clusters, but not necessary related to DEATH_EVENT. Supervised algorithms should be used if you want to make predictions.

Source Link
lpounng
lpounng
  • 1.1k
  • 3
  • 21

The reason is an unsupervised algorithm is used for a supervised problem.

Remember there is no strict right or wrong in unsupervised learning. For example, say I have a dataset of 3 attritutes, "Age", "Blood Pressure" and "Income". Both "Blood Pressure" and "Income" consists of 2 categories, "high" and "low".

There are 2 things I can do:

  1. I can set "Income" as target, and train a supervised model to predict it from "Age" and "Blood Pressure". (of course Blood Pressure may have no predictive power at all)

  2. I can also feed the 2 attributes "Age" and "Blood Pressure" into an unsupervised algorithm e.g. KMeans, and ask it to return 2 groups.

From 2., there is a chance that the algorithm gives back 2 groups, which turns out to be the two Blood Pressure clusters. Is this grouping useful to predict "Income"? Probably not. But it is not wrong either - it correctly identified 2 Blood Pressure groups. Just not related to income.

So in your case, the algorithm has detected 2 clusters, but not necessary related to DEATH_EVENT. Supervised algorithms should be used if you try to predict things.