Machine Learning for Imbalanced Data: Tackle imbalanced datasets using machine learning and deep learning techniques

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Publisher ‏ : ‎ Packt Publishing (November 30, 2023)
Language ‏ : ‎ English
Paperback ‏ : ‎ 344 pages
ISBN-10 ‏ : ‎ 1801070830
ISBN-13 ‏ : ‎ 978-1801070836
Item Weight ‏ : ‎ 1.32 pounds
Dimensions ‏ : ‎ 9.25 x 7.52 x 0.72 inches

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Machine Learning for Imbalanced Data: Tackle imbalanced datasets using machine learning and deep learning techniques

Imbalanced datasets are a common challenge in machine learning, where one class of data is significantly more prevalent than another. This can lead to biased models that perform poorly on the minority class. However, there are several techniques that can be used to address this issue and improve the performance of models on imbalanced datasets.

One approach is to use resampling techniques, such as oversampling the minority class or undersampling the majority class, to balance the dataset. Another option is to use different evaluation metrics, such as precision, recall, and F1 score, that are more suitable for imbalanced datasets.

Additionally, machine learning algorithms such as decision trees, random forests, and support vector machines can be adjusted to give more weight to the minority class, helping to improve performance on imbalanced datasets.

Deep learning techniques, such as neural networks and convolutional neural networks, can also be effective for handling imbalanced data. Techniques such as class weights, focal loss, and data augmentation can help to improve the performance of deep learning models on imbalanced datasets.

By using these techniques and approaches, machine learning practitioners can effectively tackle imbalanced datasets and build more accurate and reliable models.
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