Project- 2 , S2PAT team

[pallavisavant]

ssaurav@hawk.iit.edu (FNU Saurav) - A20536122
psavant@hawk.iit.edu (Pallavi Savant) - A20540976
ssinha20@hawk.iit.edu(Satwik Sinha)-A20547790
akutre@hawk.iit.edu (Aditya Ramchandra Kutre)-A20544809
tyerra@hawk.iit.edu(Tejaswi Yerra) -A20545536

MODEL SELECTION:
This project implements two model selection techniques:

k-Fold Cross-Validation:

Evaluates a machine learning model by splitting the dataset into ( k ) folds and using each fold as a validation set while training on the remaining ( k-1 ) folds.
The average loss (e.g., Mean Squared Error) across all folds is calculated to estimate the model’s predictive performance.
Bootstrapping:

Evaluates a model by generating multiple bootstrap samples (random sampling with replacement) from the dataset and using the out-of-bag (OOB) samples for validation.
The average error across all bootstrap iterations is computed to measure model performance.
Both methods are implemented for general-purpose models that provide fit() and predict() methods.