What is the problem with imbalanced data?

What is the problem with imbalanced data?

Imbalanced data typically refers to a classification problem where the number of observations per class is not equally distributed; often you’ll have a large amount of data/observations for one class (referred to as the majority class), and much fewer observations for one or more other classes (referred to as the …

Why accuracy is not a good measure for imbalanced class problems?

… in the framework of imbalanced data-sets, accuracy is no longer a proper measure, since it does not distinguish between the numbers of correctly classified examples of different classes. Hence, it may lead to erroneous conclusions …

How do unbalanced classed affect a machine learning model?

Most machine learning classification algorithms are sensitive to unbalance in the predictor classes. An unbalanced dataset will bias the prediction model towards the more common class!

Does class imbalance affect regression?

Logistic regression is an effective model for binary classification tasks, although by default, it is not effective at imbalanced classification.

What are the disadvantages of accuracy?

Disadvantages

• If financial Documents aren’t accurate:
• Profits may be over- or understated.
• Not all costs are accounted for.
• Investors may lose confidence in the business.
• Reputation of the business can be damaged.
• Financial statements will not be accurate.
• It can lead to cash-flow problems.

What compounds the difficultness of imbalanced classification?

The difficulty of imbalanced classification is compounded by properties such as dataset size, label noise, and data distribution. How to develop an intuition for the compounding effects on modeling difficulty posed by different dataset properties.

What is a problem that often arises in classification?

A problem that often arises in classification is the small number of training instances. This issue, often reported as data rarity or lack of data, is related to the “lack of density” or “insufficiency of information”. — Page 261, Learning from Imbalanced Data Sets, 2018.

What is cost sensitivity of misclassification errors?

This is referred to as cost sensitivity of misclassification errors and is a second foundational challenge of imbalanced classification. These two aspects, the skewed class distribution and cost sensitivity, are typically referenced when describing the difficulty of imbalanced classification.

How does the size of the dataset affect the imbalanced classification task?

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.