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| In [[machine learning]], the [[minority class]] is the less common [[label]] in a [[class-imbalanced dataset|dataset that is imbalanced]]. For example, in a [[dataset]] where there are 80% "yes" and 20% "no", "no" is the minority [[class]]. The opposite of the majority class the [[majority class]]. | | In [[machine learning]], the [[minority class]] is the less common [[label]] in a [[class-imbalanced dataset|dataset that is imbalanced]]. For example, in a [[dataset]] where there are 80% "yes" and 20% "no", "no" is the minority [[class]]. The opposite of the majority class the [[majority class]]. |
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| A [[classification model]] trained on an [[class-imbalanced dataset]] is likely to be [[biased]] towards the majority class and may not be able to [[accurately]] predict the minority one. In real-life examples, the minority class often holds greater interest due to its representation of important target variables such as fraud detection or disease diagnosis. | | A [[classification model]] trained on an [[class-imbalanced dataset]] is likely to be [[biased]] towards the majority class and may not be able to [[accurately]] predict the minority one. In real-life [[applications]], the minority class often holds greater interest due to its representation of important target variables such as fraud detection or disease diagnosis. |
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| ==Handling Minority Class in Machine Learning==
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| There are various methods for handling minorities in machine learning, such as:
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| 1. Resampling: Resampling involves either oversampling the minority class or undersampling the majority class in order to create an even dataset with equal representation from each.
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| 2. Synthetic Data Generation: This involves creating synthetic data for the minority class by employing techniques such as SMOTE (Synthetic Minority Over-sampling Technique) or ADASYN (Adaptive Synthetic Sampling). The new instances are generated by extrapolating between existing minority class instances to create new ones.
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| 3. Cost-sensitive Learning: This approach involves altering the loss function of the classifier to assign higher costs to misclassifying instances from a minority class. This encourages it to prioritize correctly classifying instances from this subclass rather than optimizing for overall accuracy.
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| 4. Ensemble Methods: Ensemble techniques such as bagging, boosting, and stacking can be employed to enhance a classifier's performance on imbalanced datasets. These involve combining multiple models into one final prediction which helps reduce the impact of class imbalance on overall performance of the classifier.
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| ==The Challenge of Minority Class== | | ==The Challenge of Minority Class== |
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| ==Approaches to Handling Minority Class== | | ==Approaches to Handling Minority Class== |
| When it comes to machine learning, there are various approaches. One popular solution is oversampling the minority class or undersampling the majority. Another strategy involves using different evaluation metrics that focus on minority performance such as precision, recall, and F1-score instead of overall accuracy. | | When it comes to dealing with the minority class in machine learning, there are various approaches. One popular solution is [[oversampling]] the minority class or [[undersampling]] the majority. Another strategy involves using different [[evaluation metrics]] that focus on minority performance such as [[precision]], [[recall]], and [[F1-score]] instead of overall [[accuracy]]. |
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| Recently, several advanced techniques have been proposed to address the minority class problem, such as ensemble methods, cost-sensitive learning and active learning. These approaches aim to enhance a model's performance on minorities by either changing the classification threshold or including additional information about them.
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| ==Explain Like I'm 5 (ELI5)==
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| Minority classes in machine learning refer to groups of children that are less in number compared to other students. For instance, if your class consists of 20 students and 18 are girls and only two boys, these students would constitute the minority group. Therefore, when splitting this classroom into two groups, make sure both contain an even number of boys and girls - this process is called "balancing the groups".
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| Similar to machine learning, when one class has fewer examples than another, we need to balance them so the computer can accurately learn and predict both classes. There are different approaches for doing this such as increasing examples from minority class or decreasing them for majority class.
| | Recently, several advanced techniques have been proposed to address the minority class problem, such as [[ensemble methods]], [[cost-sensitive learning]] and [[active learning]]. These approaches aim to enhance a model's performance on minorities by either changing the [[classification threshold]] or including additional information about them. |
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| ==Explain Like I'm 5 (ELI5)==
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| Let's say you have a bunch of toy cars and animals. The toy cars would form one group, while the animals make up another. Now imagine there are more cars than animals - this would indicate that toy cars are in the majority and all other toys form the minority group.
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| Machine learning often presents the situation where we have a wealth of data about one group but not nearly enough about another. This minority group, also referred to as the minority population, must be taken into consideration; otherwise, our models may not work as effectively for them due to insufficient information. Therefore, we need to ensure our models are fair and beneficial for both majorities and minorities alike.
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| ==Explain Like I'm 5 (ELI5)== | | ==Explain Like I'm 5 (ELI5)== |
| Machine learning utilizes computers to predict things based on past data. But sometimes, certain events happen too rarely for our computer to pick up on; these rare instances are known as the minority class. To guarantee our computer can still recognize them, we employ special methods like special glasses that enable it to see things that may otherwise go unseen. | | Machine learning utilizes computers to predict things based on past data. But sometimes, certain events happen too rarely for our computer to pick up on; these rare instances are known as the minority class. To guarantee our computer can still recognize them, we employ special methods like special glasses that enable it to see things that may otherwise go unseen. |
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| ==Explain Like I'm 5 (ELI5)==
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| Imagine you have a large basket filled with various fruits. Your favorite is apples, but there are also oranges in there as well. Upon counting up all the items in the basket, it might appear that there are more oranges than apples.
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| Machine learning often refers to data as "baskets," similar to fruit baskets. Different types of data are classified into classes, like different varieties of fruit. The class with the most data is called the majority class, while that with the least is known as the minority class.
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| Similar to how there may be more oranges than apples in a fruit basket, there may be more data in the majority class than in the minority class. When applying machine learning techniques to this data set, we must be mindful not to overlook its importance - just like you wouldn't ignore all the apples in that basket!
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| [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] | | [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] |
