docs.md

Cross Validation Techniques

Overview

Cross validation techniques are used to evaluate the performance of a model and ensure it generalizes well to unseen data. Two common methods are K-Fold Cross Validation and Leave-One-Out Validation (LOOV).

1. K-Fold Cross Validation

Process

1. Split the Dataset: Divide the dataset into K equally sized folds.
2. Training and Testing: For each fold:
   • Train the model on K-1 folds.
   • Test the model on the remaining fold.
3. Evaluate Performance: Compute performance metrics for each fold.
4. Average the Results: Average the metrics across all K iterations.

Data Loaders

• the folder fold_dataloaders has the pth files for all the folds, the content of the file is a dictionary having train_dataset and test_dataset as keys to access the respective dataloaders.

• Useage of the pth files.

import os 
from dataloader_1 import *
#other necessary imports

for fold in os.listdir(filepath):
    file=filepath+'\\'+fold
    train,test=load_fold_data(file,batch_size)
    ##trainin loop as usual 


    ##testing on the test data

    ##printing the results for the particualr fold(averaged over the entire fold)

##printing the results for the entire dataset(averaged over all the folds)

2. Leave-One-Out Validation (LOOV)

Process

1. Split the Dataset: Each data point is treated as a single fold.
2. Training and Testing: For each data point:
   • Train the model on all other data points.
   • Test the model on the single data point.
3. Evaluate Performance: Compute performance metrics for each iteration.
4. Average the Results: Average the metrics across all iterations.

Data Loaders

• follow the same procedure as shown above to access the dataloaders from the folders.

By using these techniques, our model's performance is evaluated in a comprehensive and unbiased manner, which could potentially give good results.