Unbalanced data is only a problem depending on your application. If for example your data indicates that A happens 99.99% of the time and 0.01% of the time B happens and you try to predict a certain result your algorithm will probably always say A. This is of course correct! It is unlikely for your method to get better prediction accuracy than 99.99%. However in many applications we are not interested in just the correctness of the prediction but also in why B happens sometimes. This is where unbalanced data becomes a problem. Because it is hard to convince your method that it can predict better than 99.99% correct. The method is correct but not for your question. So solving unbalanced data is basically intentionally biasing your data to get interesting results instead of accurate results. All methods are vulnerable although SVM and logistic regressions tend to be a little less vulnerable while decision trees are very vulnerable.

In general there are three cases:

1. your purely interested in accurate prediction and you think your data is reprenstative. In this case you do not have to correct at all, Bask in the glory of your 99.99% accurate predictions :).

2. You are interested in prediction but your data is from a fair sample but somehow you lost a number of observations. If you lost observations in a completely random way you're still fine. If you lost them in a biased way but you don't know how biased, you will need new data. However if these observations are lost only on the basis of one charateristic. (for example you sorted results in A and B but not in any other way but lost half of B) Ypu can bootstrap your data.

3. You are not interested in accurate global prediction, but only in a rare case. In this case you can inflate the data of that case by bootstrapping the data or if you have enough data throwing a way data of the other cases. Notice that this does bias your data and results and so chances and that kind of results are wrong!

In general it mostly depends on what the goal is. Some goals suffer from unbalanced data others don't. All general prediction methods suffer from it because otherwise they would give terrible results in general.

Unbalanced data is only a problem depending on your application. If for example your data indicates that A happens 99.99% of the time and 0.01% of the time B happens and you try to predict a certain result your algorithm will probably always say A. This is of course correct! It is unlikely for your method to get better prediction accuracy than 99.99%. However in many applications we are not interested in just the correctness of the prediction but also in why B happens sometimes. This is where unbalanced data becomes a problem. Because it is hard to convince your method that it can predict better than 99.99% correct. The method is correct but not for your question. So solving unbalanced data is basically intentionally biasing your data to get interesting results instead of accurate results. All methods are vulnerable although SVM and logistic regressions tend to be a little less vulnerable while decision trees are very vulnerable.

In general there are three cases:

1. You are purely interested in accurate prediction and you think your data is representative. In this case you do not have to correct at all. Bask in the glory of your 99.99% accurate predictions :).

2. You are interested in prediction but your data is from a fair sample but somehow you lost a number of observations. If you lost observations in a completely random way you're still fine. If you lost them in a biased way but you don't know how biased, you will need new data. However if these observations are lost only on the basis of one charateristic. (for example you sorted results in A and B but not in any other way but lost half of B) Ypu can bootstrap your data.

3. You are not interested in accurate global prediction, but only in a rare case. In this case you can inflate the data of that case by bootstrapping the data or if you have enough data throwing a way data of the other cases. Notice that this does bias your data and results and so chances and that kind of results are wrong!

In general it mostly depends on what the goal is. Some goals suffer from unbalanced data others don't. All general prediction methods suffer from it because otherwise they would give terrible results in general.

Unbalanced data is only a problem depending on your application. If for example your data indicates that A happens 99.99% of the time Aand 0.01% of the time B happens and you try to predict a certain result your algorithm will probably always say A. This is ofcource correct! It is unlikely for your method to get better prediction accuracy than 99.99%. However in many applications we are not interested in just the correctness of the predicition but also in why B happens sometimes. This is where unbalanced data becomes a problem. Because it is hard to convince your method that it can predict better than 99.99% correct. The method is correct but not for your question. So solving unbalanced data is basically intentionally biasing your data to get interesting results instead of accurate results. All methods are vulnerable although SVM and logistic regressions tend to be a little less vulnerable while decision trees are very vulnerable.

In general there are three cases:

1. your purely interested in accurate prediction and you think your data is reprenstative. In this case you do not have to correct at all, Bask in the glory of your 99.99% accurate predictions :).

2. You are interested in prediction but your data is from a fair sample but somehow you lost a number of observations. If you lost observations in a completely random way you're still fine. If you lost them in a biased way but you don't know how biased, you will need new data. However if these observations are lost only on the basis of one charateristic. (for example you sorted results in A and B but not in any other way but lost half of B) Ypu can bootstrap your data.

3. You are not interested in accurate global prediction, but only in a rare case. In this case you can inflate the data of that case by bootstrapping the data or if you have enough data throwing a way data of the other cases. Notice that this does bias your data and results and so chances and that kind of resukts are wrong!

In general it mostly depends on what the goal is. Some goals suffer from unbalanced data others don't. All general prediction methods suffer from it because otherwise they would give terrible results in general.

Unbalanced data is only a problem depending on your application. If for example your data indicates that A happens 99.99% of the time and 0.01% of the time B happens and you try to predict a certain result your algorithm will probably always say A. This is of course correct! It is unlikely for your method to get better prediction accuracy than 99.99%. However in many applications we are not interested in just the correctness of the prediction but also in why B happens sometimes. This is where unbalanced data becomes a problem. Because it is hard to convince your method that it can predict better than 99.99% correct. The method is correct but not for your question. So solving unbalanced data is basically intentionally biasing your data to get interesting results instead of accurate results. All methods are vulnerable although SVM and logistic regressions tend to be a little less vulnerable while decision trees are very vulnerable.

In general there are three cases:

1. your purely interested in accurate prediction and you think your data is reprenstative. In this case you do not have to correct at all, Bask in the glory of your 99.99% accurate predictions :).

2. You are interested in prediction but your data is from a fair sample but somehow you lost a number of observations. If you lost observations in a completely random way you're still fine. If you lost them in a biased way but you don't know how biased, you will need new data. However if these observations are lost only on the basis of one charateristic. (for example you sorted results in A and B but not in any other way but lost half of B) Ypu can bootstrap your data.

3. You are not interested in accurate global prediction, but only in a rare case. In this case you can inflate the data of that case by bootstrapping the data or if you have enough data throwing a way data of the other cases. Notice that this does bias your data and results and so chances and that kind of results are wrong!

In general it mostly depends on what the goal is. Some goals suffer from unbalanced data others don't. All general prediction methods suffer from it because otherwise they would give terrible results in general.