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Imbalanced datasets indeed might damage the predictions. The reason is that when the dataset is imbalance the majority rule gets high accuracy and learner are biased toward it and perform badly on the minority class. You might be interested in this Editorial: Special Issue on Learning from Imbalanced Data Sets and Learning from Imbalanced Data

You have 30K samples of the minority class (3%) so are dealing with a relative imbalance and not absolute imbalance. These are good new since in such scenarios you are likely to end up with enough data after downsampling.

Your intuition of balancing the set was correct - in order to differ between the majority and the minority classes. However, your model is needed to perform well not in differing between them in a balance distribution but on their natural distribution. You can adapt your model back to the natural distribution by learning a new model that will do this adaptation. For details see here.

For the usage of different datasets in order to learn and validate see herehere

Imbalanced datasets indeed might damage the predictions. The reason is that when the dataset is imbalance the majority rule gets high accuracy and learner are biased toward it and perform badly on the minority class. You might be interested in this Editorial: Special Issue on Learning from Imbalanced Data Sets and Learning from Imbalanced Data

You have 30K samples of the minority class (3%) so are dealing with a relative imbalance and not absolute imbalance. These are good new since in such scenarios you are likely to end up with enough data after downsampling.

Your intuition of balancing the set was correct - in order to differ between the majority and the minority classes. However, your model is needed to perform well not in differing between them in a balance distribution but on their natural distribution. You can adapt your model back to the natural distribution by learning a new model that will do this adaptation. For details see here.

For the usage of different datasets in order to learn and validate see here

Imbalanced datasets indeed might damage the predictions. The reason is that when the dataset is imbalance the majority rule gets high accuracy and learner are biased toward it and perform badly on the minority class. You might be interested in this Editorial: Special Issue on Learning from Imbalanced Data Sets and Learning from Imbalanced Data

You have 30K samples of the minority class (3%) so are dealing with a relative imbalance and not absolute imbalance. These are good new since in such scenarios you are likely to end up with enough data after downsampling.

Your intuition of balancing the set was correct - in order to differ between the majority and the minority classes. However, your model is needed to perform well not in differing between them in a balance distribution but on their natural distribution. You can adapt your model back to the natural distribution by learning a new model that will do this adaptation. For details see here.

For the usage of different datasets in order to learn and validate see here

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DaL
DaL
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Imbalanced datasets indeed might damage the predictions. The reason is that when the dataset is imbalance the majority rule gets high accuracy and learner are biased toward it and perform badly on the minority class. You might be interested in this Editorial: Special Issue on Learning from Imbalanced Data Sets and Learning from Imbalanced Data

You have 30K samples of the minority class (3%) so are dealing with a relative imbalance and not absolute imbalance. These are good new since in such scenarios you are likely to end up with enough data after downsampling.

Your intuition of balancing the set was correct - in order to differ between the majority and the minority classes. However, your model is needed to perform well not in differing between them in a balance distribution but on their natural distribution. You can adapt your model back to the natural distribution by learning a new model that will do this adaptation. For details see here.

For the usage of different datasets in order to learn and validate see here