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nbro
nbro
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Classification vs Do I need classification or regression machine learningto predict the availability of a user given some features?

While studying data mining methods I have come to understand that there are two main categories:

-Predictive methods:

  • classificationPredictive methods:

  • Classification

  • Regression

-Descriptive methods:

  • Descriptive methods:

  • Clustering

  • Association rules

Since I want to predict the user availability (output) based on location, activity, battery level  (input for the training model), I think it's obvious that I would choose "Predictive methods", but now I can't seem to choose between classification and regression. From From what I understand this far, classification can solve my problem, because the output is "available" or "not available".

First question is: can classification provide me with the probability/likelihood of the user being available or not available?Can classification provide me with the probability (or likelihood) of the user being available or not available?

As in the output wouldn't just be 0  (not available) or 1 (for available), but it's be something like:

  • 80%$80\%$ available
  • 20%$20\%$ not available

Second question is, can this problem also be solved using regression?Can this problem also be solved using regression?

I get that regression is used for continuous output (not just 0 or 1 outputs), but can't the output be the continuous value of the user availability? like (like the output being 80$80$ meaning user is 80% available (implicitly$80\%$ available, implicitly the user is 20%$20\%$ unavailable).

Classification vs regression machine learning?

While studying data mining methods I have come to understand that there are two main categories:

-Predictive methods:

  • classification

  • Regression

-Descriptive methods:

  • Clustering

  • Association rules

Since I want to predict the user availability (output) based on location, activity, battery level(input for the training model) I think it's obvious that I would choose "Predictive methods" but now I can't seem to choose between classification and regression. From what I understand this far, classification can solve my problem because the output is "available" or "not available".

First question is: can classification provide me with the probability/likelihood of the user being available or not available?

As in the output wouldn't just be 0(not available) or 1 (for available) but it's be something like:

  • 80% available
  • 20% not available

Second question is, can this problem also be solved using regression?

I get that regression is used for continuous output (not just 0 or 1 outputs) but can't the output be the continuous value of the user availability? like the output being 80 meaning user is 80% available (implicitly the user is 20% unavailable)

Do I need classification or regression to predict the availability of a user given some features?

While studying data mining methods I have come to understand that there are two main categories:

  • Predictive methods:

  • Classification

  • Regression

  • Descriptive methods:

  • Clustering

  • Association rules

Since I want to predict the user availability (output) based on location, activity, battery level  (input for the training model), I think it's obvious that I would choose "Predictive methods", but now I can't seem to choose between classification and regression. From what I understand this far, classification can solve my problem, because the output is "available" or "not available".

Can classification provide me with the probability (or likelihood) of the user being available or not available?

As in the output wouldn't just be 0  (not available) or 1 (for available), but it's be something like:

  • $80\%$ available
  • $20\%$ not available

Can this problem also be solved using regression?

I get that regression is used for continuous output (not just 0 or 1 outputs), but can't the output be the continuous value of the user availability (like the output being $80$ meaning user is $80\%$ available, implicitly the user is $20\%$ unavailable).

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Guest2000
Guest2000
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Classification vs regression machine learning?

While studying data mining methods I have come to understand that there are two main categories:

-Predictive methods:

  • classification

  • Regression

-Descriptive methods:

  • Clustering

  • Association rules

Since I want to predict the user availability (output) based on location, activity, battery level(input for the training model) I think it's obvious that I would choose "Predictive methods" but now I can't seem to choose between classification and regression. From what I understand this far, classification can solve my problem because the output is "available" or "not available".

First question is: can classification provide me with the probability/likelihood of the user being available or not available?

As in the output wouldn't just be 0(not available) or 1 (for available) but it's be something like:

  • 80% available
  • 20% not available

Second question is, can this problem also be solved using regression?

I get that regression is used for continuous output (not just 0 or 1 outputs) but can't the output be the continuous value of the user availability? like the output being 80 meaning user is 80% available (implicitly the user is 20% unavailable)