Handling Imbalanced Data: Strategies to Tackle a Common ML Challenge

Every person working with data wants to create accurate data predictive models. The quality of machine learning models depends on the data you provide. Thus, preparing the data is arguably the most significant phase in data science. There are numerous hurdles that you may face with datasets, such as feature selection, feature engineering, encryption, reducing dimensionality, and so on, and among the most prevalent categorization challenges is imbalanced data.

There are chances where imbalanced data is unavoidable, such as fraudulent cases where the non-fraudulent cases will be the majority class, and frauds will be the minority class. Here, the frauds matter (minority class), and thus, you would need to handle the imbalanced data and train the algorithms to run accordingly.

Here is what we are going to cover in this article

• What is imbalanced data in machine learning?
• Why is imbalanced data in machine learning an obstacle?
• How to deal with imbalanced data using sampling technique?

• Random under-sampler
• Synthetic minority oversampling technique (SMOTE)
• Random over-sampler

• How to deal with unbalanced data using different techniques?

• Ensemble learning technique
• Cost-sensitive learning technique
• Confusion matrix
• SMOGN

• Gear up for your next machine learning interview
• FAQs on how to deal with imbalanced data

What is Imbalanced Data in Machine Learning?

An imbalanced data typically results from an uneven distribution of classes. While a small amount of imbalance would not pose a challenge, a large amount of imbalance might cause problems with the way we categorize forecasts. This is due to the fact that most machine learning algorithms require a large amount of data. When a number of the classes have insufficient data, the algorithm cannot forecast what will happen properly.

The goal of classification models is to group data into distinct categories. A dataset that is unbalanced has a single bucket that occupies a disproportionately high amount of the initial training data and one bucket that is undervalued.

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A model that has been trained on unbalanced data has the difficulty of learning that it has the ability to forecast the majority class with significant accuracy, even though in real-world scenarios, detecting the minority class is equally essential as identifying the majority class.

In the actual world, imbalanced datasets are typical. These datasets often generate forecasts that are skewed and degrade the machine learning model’s efficacy as a whole. The degree of imbalance may differ greatly and be brought on by various things, including naturally uneven distribution or biased sampling during data gathering.

Why is Imbalanced Data in Machine Learning an Obstacle?

The prediction models created using standard methods for machine learning may be biased and erroneous. They have a tendency to generate subpar classifiers when provided with an imbalanced dataset. Conventional classifier techniques, such as Decision Tree and Logistic Regression, are biased towards classes with several examples.

The characteristics of the minority class (or imbalanced data) are often dismissed as noise. As a result, the minority class is more likely to be incorrectly classified than the dominant class. This occurs as a result of the fact that machine learning algorithms are typically created to reduce error and increase accuracy.

How to Deal with Imbalanced Data Using Sampling Techniques?

Dealing with imbalanced data can be overwhelming at times. There are certain sampling techniques that can be used for dealing with imbalanced datasets in machine learning:

Random Under-Sampler

The Random Under-Sampler downsamples the more extensive classes in the most efficient manner feasible by randomly selecting samples from every class. The sample size is versatile because it is determined as a part of what constitutes appropriate class balance criteria.

RU generally makes sure that no data is fabricated and that all output data is a selected portion of the initial input dataset. However, with large levels of imbalance, this typically results in a significant reduction in the amount of training data that can be used, which eventually results in decreased model efficacy.

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Synthetic Minority Oversampling Technique (SMOTE)

The basic principle of SMOTE is that created cases must be built from accessible findings, but they shouldn’t be exact replicas. After an SVM classifier has been trained on the first training data set, borderline regions are roughly determined by support vectors.

Following computation, samples are created close to the predicted boundary. SMOTE has demonstrated extensive use and outstanding success in a variety of applications and functions.

Random Over-Sampler

The Random Over-Sampler algorithm uses the method of oversampling smaller sets up until the class levels are matched.

RO overcomes the challenge of data destruction. However, since samples have now been recurred in the dataset, it creates even additional bias. As a result, there is a bias to concentrate on the accurate characteristic values of the recurrent samples.

How to Deal with Unbalanced Data Using Different Techniques?

There are certain techniques other than sampling techniques that are used for dealing with imbalanced data:

Ensemble Learning Technique

The ensemble-based approach is implemented for handling unbalanced data sets. To enhance the efficacy of a particular classifier, it is merged with the outcome or performance of numerous classifiers. It mostly synthesizes the results of various fundamental learners. Ensemble learning can be done in a variety of ways, including Boosting and Bagging.

1. Bagging or Bootstrap Combining attempts to apply comparable learners to a small dataset before taking the average of each of the predictions.
2. The Boosting (Adaboost) technique iteratively adjusts the significance of an observation in accordance with the most recent categorization.

Cost-Sensitive Learning Technique

This technique aims to categorize data into an array of recognized classes with a high degree of accuracy. It plays an essential part in machine learning methods, covering real-world uses for data mining. By lowering the overall cost, it considers the costs associated with misclassification.

Confusion Matrix

The confusion matrix establishes the foundation for performance measurements for challenges with binary classification. Most performance parameters, including precision, misclassification rates, accuracy, and recollection, are obtained from the confusion matrix.

However, if the data is unbalanced, accuracy is inadequate. Since the model can forecast the majority class more precisely than the minority class, which is typically the class we are concerned about the most, it may achieve greater precision at the expense of the minority class.

SMOGN

The main idea behind the SMOGN method is to generate hypothetical instances by combining the SMOTER and Gaussian Noise approaches. It concurrently limits potential risks that SMOTER may face, including a lack of different instances, by employing the more mindful technique of incorporating Gaussian Noise.

Whenever the initial example and the chosen k-nearest neighbor are near enough, it generates new engineered instances using SMOTER, and when they are further away, it uses Gaussian noise.

Gear Up for Your Next Machine Learning Interview

Both data science and machine learning operations have to deal with imbalanced data, which necessitates the use of efficient methods and tools to provide precise predictions. You may enhance the performance of your models and gain useful insights through your data by comprehending the reasons behind imbalanced data along with its challenges and solutions. Interview Kickstart has designed a perfect machine learning program that can help you understand how to deal with imbalanced data. You might also learn about the various machine learning algorithms and models. It also prepares you for several machine learning interviews you can have with leading tech giants.

FAQs on How to Deal with Imbalanced Data

Q1. Which algorithm is best for imbalanced data?

Tree-based algorithms are often the best for handling imbalanced data. Additionally, Boosting algorithms like AdaBoost and XGBoost are also the ideal choice for imbalanced datasets because they give higher significance to minority classes.

Q2. Does imbalanced data cause overfitting?

When using imbalanced data, overfitting is a prevalent challenge. It happens when a model gets overly detailed and starts to pick up on the noise and anomalies of training data, which makes it perform poorly on unknown data.

Q3. Does imbalanced data affect accuracy?

Imbalance data significantly affects the data accuracy in machine learning models and algorithms.

Q4. Does SMOTE cause overfitting?

Although SMOTE can somewhat increase the precision of the minority class, it runs the risk of producing noisy cases and overfitting issues because the distribution of nearby data is not taken into account.

Q5. Why use F1 for imbalanced data?

F1 can relay the true performance of the model when the dataset is imbalanced.

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