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Improving Model Generalization with Cross-Validation: A Deep Dive

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Introduction In machine learning, a high accuracy on the training set does not always mean a good model . A model may perform well on training data but fail to generalize to unseen data—this is known as overfitting . To solve this, cross-validation is one of the most effective techniques. It ensures that the model is evaluated on different subsets of data, providing a better estimate of performance and improving generalization . In this blog, we will explore: What cross-validation is and why it is important? Different types of cross-validation techniques. How to implement cross-validation in Python (Scikit-Learn & TensorFlow)? Best practices for improving model performance. 1. What is Cross-Validation? Cross-validation (CV) is a resampling technique that helps evaluate a model by splitting the dataset into multiple training and validation sets . Instead of training the model on a single training set and testing it once, CV allows multiple training-validation cycles, providing...
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Optimizing Transformer-Based Models for Medical Image Analysis

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Introduction Medical image analysis has significantly advanced with deep learning, particularly transformer-based models like Compact Convolutional Transformers (CCT) . These models offer improved feature extraction and attention mechanisms, making them effective for tasks like disease detection from radiological images . However, optimising these models requires careful tuning of hyperparameters, augmentation techniques, and regularization methods . In this blog, we will explore key strategies to optimize transformer-based models for medical image classification , ensuring higher accuracy and better generalization. 1. Data Preprocessing and Augmentation Medical datasets are often imbalanced and limited , making preprocessing essential. Here’s how you can improve model robustness: a) Handling Imbalanced Data Oversampling (e.g., SMOTE) or undersampling techniques to balance class distribution. Using weighted loss functions to give more importance to underrepresented classes. ...
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