How to Handle Skewed Data in Machine Learning
Data is the fuel that drives the success of machine learning algorithms. But not all data is created equal. One problem that can arise when working with datasets is skewed data. Skewed data occurs when the distribution of a variable is not evenly distributed, resulting in an unbalanced data set. This can negatively affect the performance of machine learning algorithms because they cannot learn patterns from data effectively. In this blog post, we explore what skewed data is and how it is handled in machine learning.
What is Skewed Data?
In statistics, skewness is a measure of the asymmetry of a probability distribution. If the distribution is the same, then the mean, median, and formula are all the same. However, if the distribution is skewed, the mean, median, and mode are different. Skewed data can be positively or negatively skewed. Positive skewness means that the tails of the distribution are longer on the positive side than on the negative side. A negative skew means that the tail of the distribution is longer on the negative side than on the positive side.
Why is Skewed Data a Problem?
Skewed data can cause problems for machine learning algorithms, as they are designed to look for patterns in data. If the data is unbalanced, the algorithm can only learn the patterns of the majority class, while completely ignoring the minority class. This can lead to biased results and incorrect expectations. For example, in a data set on credit card fraud, the majority of transactions may be legitimate, with only a small fraction fraudulent. If the algorithm is trained on this unbalanced data set, it may misclassify all transactions as legitimate and ignore fraudulent transactions.
How to Handle Skewed Data?
There are multiple ways to that can be used to handle skewed data in machine learning, some of them are:
1 – Resampling Techniques:
Resampling techniques can be used to solve the problem of imbalanced data. There are two types of resampling: oversampling and undersampling. Oversampling involves adding data to a minority class to balance the data set. Undersampling involves removing data from the majority class to balance the data set. These techniques can be useful in situations where the data set is highly biased.
2 – Using Appropriate Performance Metrics:
In imbalanced datasets, accuracy is not always the best performance metric to evaluate model performance. Other metrics such as precision, recall and F1 scores may be more informative.
3 – Using Different Algorithms:
Different algorithms can be more or less sensitive to imbalanced data. For example, decision trees and naive Bayes can perform well with imbalanced data, while k-nearest neighbour and support vector machines may not.
4 – Using Data Transformations:
Transforming the data can also help to reduce skewness. Common transformations include the logarithmic, square root, and reciprocal transformations.
Example of Handling Skewed Data:
Let’s consider a dataset of customer churn, where the target variable is whether a customer has cancelled their subscription or not. Suppose the dataset has 1000 observations, with 900 belonging to the “No churn” class and 100 belonging to the “Churn” class. The dataset is heavily skewed towards the “No churn” class.
To address the skewed data, we can use oversampling to add more data to the “Churn” class. One common oversampling technique is the Synthetic Minority Oversampling Technique (SMOTE). SMOTE creates synthetic examples of the minority class by interpolating between existing examples. After applying SMOTE, the dataset may have 900 observations in each class, resulting in a balanced dataset.
Skewed data can be a challenge in machine learning, but there are several techniques to address it. By using appropriate performance metrics, resampling techniques, data transformations, and different algorithms, we can improve the performance of our models on imbalanced datasets.