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Why is it important to have a balanced dataset?
From the above examples, we notice that having a balanced data set for a model would generate higher accuracy models, higher balanced accuracy and balanced detection rate. Hence, its important to have a balanced data set for a classification model.
What is a balanced data?
A balanced data set is a set that contains all elements observed in all time frame. Whereas unbalanced data is a set of data where certain years, the data category is not observed.
When is a classification dataset is imbalanced?
Most classification data sets do not have exactly equal number of instances in each class, but a small difference often does not matter. There are problems where a class imbalance is not just common, it is expected. For example, in datasets like those that characterize fraudulent transactions are imbalanced.
Which is an example of an imbalanced classification problem?
An imbalanced classification problem where the distribution of examples is uneven by a small amount in the training dataset (e.g. 4:6). Severe Imbalance. An imbalanced classification problem where the distribution of examples is uneven by a large amount in the training dataset (e.g. 1:100 or more).
Is it bad to have imbalanced data sets?
Imbalanced data is not always a bad thing, and in real data sets, there is always some degree of imbalance. That said, there should not be any big impact on your model performance if the level of imbalance is relatively low. Now, let’s cover a few techniques to solve the class imbalance problem. Evaluation metrics can be applied such as:
Why is imbalanced classification difficult in machine learning?
As such, the size of the dataset dramatically impacts the imbalanced classification task, and datasets that are thought large in general are, in fact, probably not large enough when working with an imbalanced classification problem. Without a sufficient large training set, a classifier may not generalize characteristics of the data.