Complex technologies such as deep learning used to be a kind of black-box model since you couldn't have a thorough idea of what was happening inside. However, tools like SHAP (SHapely Additive exPlanations) make it a thing of the past. With SHAP, you can easily interpret the predictions of deep learning models with minimal coding. CNNs aren't among the most straightforward concepts to understand. A network using mathematical calculations learns the kernels for images and detects the useful patterns to classify unseen images correctly.

Imbalanced datasets are a special case for classification problem where the class distribution is not uniform among the classes. One of the techniques to handle imbalance datasets is data sampling. Synthetic Minority Oversampling Technique (SMOTE) is an oversampling technique that generates synthetic samples from the minority class to match the majority class. It is used to obtain a synthetically class-balanced or nearly class-balanced training set. SMOTE works by selecting examples that are close in the feature space, drawing a line between the examples in the feature space and drawing a new sample at a point along that line.