When is a dataset considered to be imbalanced?

When is a dataset considered to be imbalanced?

Generally, a dataset for binary classification with a 49–51 split between the two variables would not be considered imbalanced. However, if we have a dataset with a 90–10 split, it seems obvious to us that this is an imbalanced dataset. Clearly, the boundary for imbalanced data lies somewhere between these two extremes.

How does the presence of this dataset complicate matters?

As you may have suspected, most people that already pay for Netflix don’t have a 50% chance of stopping their subscription every month. In fact, the percentage of people that will churn is rather small, closer to a 90–10 split. How does the presence of this dataset imbalance complicate matters?

What should be the accuracy of a balanced dataset?

In an ideal world (at least for the data scientist), our training and testing datasets would be close to fully balanced, having around 50% of the dataset containing individuals that will churn and 50% who will not. In this case, a 90% accuracy will more or less indicate a 90% accuracy on both the positively and negatively classed groups.

What happens if there is missing data in a data set?

If there is missing data elsewhere in the data set, the existing values are used. Since a pairwise deletion uses all information observed, it preserves more information than the listwise deletion. Pairwise deletion is known to be less biased for the MCAR or MAR data.

How to build a predictive model with imbalanced data?

Building a predictive model with imbalanced data – Matthew Lim – A blog to detail my data analysis/data science projects. Imbalanced data typically refers to a model with classification problems where the classes are not represented equally (e.g. 90% of the data belongs to one class).

Can a classification algorithm work with imbalanced data?

Working on an imbalanced dataset tends to be extremely tricky as simple classification algorithms tend to struggle in such situations, usually resulting in overfitting on the majority class and completely ignoring the minority class.