Explore the latest advancements in data science techniques, trends, and tools.
Feature engineering is the process of selecting, transforming, and creating features to improve the performance of machine learning models. It's crucial for achieving high accuracy and efficiency.
A good feature engineering strategy often starts with understanding the data and the problem you're trying to solve.
Gradient boosting is a powerful ensemble learning technique that combines multiple weak learners into a strong learner. It offers significantly better performance than traditional methods.
Key aspects include boosting, trees, and regularization.
Keras is a high-level API for building and training neural networks. It simplifies the process of working with TensorFlow and PyTorch.
It allows you to focus on the model architecture, not the underlying framework.
Common model evaluation metrics include accuracy, precision, recall, F1-score, and AUC. Choosing the right metric depends on the specific problem.