A dataset has imbalanced dataset. How do we prove this? Is there any statistical or mathematical or visual representation approaches?
An imbalanced dataset could be normalized through transformation (Genstat) and I'm sure this dataset was taken on biological organisms. First, you could draw normal distribution curves and check if it follows logical trend ie that assumptions of Analysis of variance.
You can get an idea here
https://machinelearningmastery.com/failure-of-accuracy-for-imbalanced-class-distributions/
Hi, Mohamed. You can check a normal distribution by means of normality tests such as the Shapiro-Wilk normality test, which is assumed to be the most accurate one for almost all small to large sample sizes. The Sig. value of this test should be less than 0.05 to claim that a data set is not normally distributed. Additionally, you can check the values of the skewness and kurtosis divided by their standard errors; if the value does not fall within ±1.96 for p < 0.05, ±2.58 for p < 0.01, and ±3.29 for p < 0.001, it can be concluded that the distribution is not normal. Visually, you can also check the histogram and the normal Q-Q plot.
If when dividing the numerical scales into equal and adaptive intervals, significantly different models are obtained, then the data is unbalanced
In Machine Learning, data imbalance is an issue if you want to perform classification. Suppose you want to classify data into two classes, A and B. The dataset is imbalanced if the prior probabilities of the classes are equal to 0.5, i.e. if you pick randomly one item in the dataset, the probability that it belongs to class A is equal to the probability that it belongs to the other class. Therefore, finding out whether the dataset is balanced or imbalanced is extremely simple: just count the number of items of the dataset that belong to class A and the number of items that belong to class B; if these numbers are roughly equal, the dataset is balanced, otherwise it is imbalanced. It's as simple as that. If there are more than two classes, just check whether the numbers of items in each class are roughly equal. Forget about normality tests, adaptive intervals, and Genstat advertising.
Thank you Gerard Dreyfus. Further,
If we have more than one features or parameters, will it be correct?
Hi Mohamed Parvees
You analyze data through EDA technique.
Imbalance Ratio: majority class / minority class
You will get like 1:10 IR ratio, means for every minority data point there 10 majority data points.
Kailas Hambarde
1) There is much, much, much more to Exploratory Data Analysis (EDA) than just counting the number of examples per class. Using fancy acronyms instead of simple terms to describe simple things is a sure way of making simple things confusing.
2) The definition of the Imbalance Ratio that you provide is wrong. It is definitely not the ratio of the number of elements of the majority class to the number of elements of the minority class. It is its reciprocal. Providing erroneous information is another way of making simple things confusing.
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