pokemaster720 / dm-hw-1-q-2

DM-HW-1-Q-2

This code has two main functionalities. The first function, create_subset_files, is designed to create smaller training files from a large original dataset. The second function is to perform_cross_validation, it then performs a k-fold cross-validation using ridge regression to select the best hyperparameter value for the regularization strength (lambda).

Here's a step-by-step explanation of each part of the code:

create_subset_files Function

1. original_file: The CSV file containing the original dataset.
2. instances_list: A list containing different sizes of subsets to create from the original dataset.
3. The pd.read_csv method reads the original CSV file into a DataFrame.
4. The for loop iterates over each size specified in instances_list.
5. data.head(instances) selects the first 'instances' number of rows from the DataFrame.
6. subset_file_name creates a filename for the subset. It is formatted with the number of instances and fixed parts of the filename. The pattern assumes subsets will always have a "(1000)-100" naming scheme which might not be true for different datasets.
7. subset.to_csv writes the subset DataFrame to a CSV file without the index column.

perform_cross_validation Function

1. kf = KFold(...): It Initializes the KFold object for cross-validation with 10 splits and a fixed random state for reproducibility.
2. lambda_values: A list of potential regularization strengths (alpha) for the Ridge regression model.
3. The function iterates over each lambda value and performs cross-validation:
   • mse_list collects mean squared error (MSE) values for each fold.
   • The nested for loop uses the indices provided by kf.split(X) to split the data into training and validation sets (X_train, X_val, y_train, y_val).
   • A Ridge regression model is trained for each lambda value on the training set and then used to predict on the validation set.
   • The MSE for the validation predictions is computed and stored in mse_list.
   • The average MSE over all folds for the current lambda is calculated.
   • If the average MSE is smaller than the current best MSE, best_lambda and best_mse are updated.
4. The function returns best_lambda and best_mse, which represent the optimal lambda value and its corresponding MSE, respectively.

Main Execution

• The instances_list is defined, with example subset sizes.
• create_subset_files function is called to create subset files.
• Placeholder numpy arrays X_placeholder and y_placeholder are created to simulate features and labels.
• The perform_cross_validation function is called with these placeholders and hypothetical lambda_values.
• Finally, it prints the best lambda value and its corresponding MSE found from cross-validation.

Considerations and Usage

• The placeholder data should be replaced with actual feature matrices and label vectors from the prepared dataset.
• lambda_values should be a range that is sensible for the given problem or determined using domain knowledge.
• When using the create_subset_files function in practice, the naming pattern for the CSV files should adapt to the actual dataset, not just the predefined pattern.

This code is written in Python, utilizing libraries such as pandas for data handling, scikit-learn for machine learning algorithms and evaluation, and numpy for numerical operations.

It is typical for machine learning applications where training on very large datasets and tuning regularization parameters are necessary tasks