Discuss Holdout method and random sampling methods.

Certainly, here's a concise summary of each method in a pointwise format:

Holdout Method

• Purpose: Evaluate machine learning model performance on unseen data.
• Process: Split dataset into two subsets - approximately 70% training and 30% testing.
• Evaluation: Test the model on the testing set after training on the training set.
• Advantages:
  • Simple to implement.
  • Faster and less computationally demanding.
• Disadvantages:
  • Inefficient data usage, especially with small datasets.
  • Performance estimate can vary greatly based on the data split.

Random Sampling Methods

• Types:
  • Simple Random Sampling: Equal chance for each data point to be selected.
  • Stratified Sampling: Divides dataset into strata and samples from each to maintain representation.
  • Cluster Sampling: Divides into clusters, randomly selects entire clusters.
• Application:
  • Used in cross-validation to ensure varied training and testing subsets.
  • Enhances model robustness by training on diverse data samples.