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Minority class: Difference between revisions
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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. | 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. | ||
==The Challenge of Minority Class== | ==The Challenge of Minority Class== | ||
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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. | 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. | ||
==Explain Like I'm 5 (ELI5)== | |||
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". | |||
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. | |||
==Explain Like I'm 5 (ELI5)== | |||
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. | |||
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. | |||
==Explain Like I'm 5 (ELI5)== | ==Explain Like I'm 5 (ELI5)== | ||