When dealing with huge datasets, scalable, and efficient methods are necessary. Three popular methods are:
Stochastic Gradient Descent (SGD) - It's an efficient optimization method used in training large-scale machine learning models, especially in deep learning.
Random Forest - These models work by building a bunch of decision trees and combining their predictions, making them robust and suitable for large datasets.
Dimensionality Reduction Techniques - Techniques like Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) can help reduce the number of features in the dataset, making it more manageable.