Question

How can you handle large-scale datasets in machine learning?

Answer 1

Handling large-scale datasets requires careful consideration of computational resources and efficient data processing techniques. Some common approaches include:

• Data partitioning: Splitting the dataset into smaller partitions that can fit into memory or be processed in parallel.
• Sampling: Working with representative subsets of the data instead of the entire dataset.
• Data compression: Using techniques like lossy or lossless compression to reduce the storage requirements of the dataset.
• Distributed storage: Storing the dataset across multiple machines or in a distributed file system like Hadoop Distributed File System (HDFS).