Documentation for get_prediction_plot Function

Function Purpose

The get_prediction_plot function performs model building and prediction for a dataset using a random forest model. It iterates over multiple test repetitions, trains the model on the training data, and makes predictions on the test data. The function then generates a histogram to visualize the distribution of predictions for the outcome variable (LIVER).

Input Parameters

The function accepts the following input parameters:

Parameter	Description	Type
Data	The dataset to use for training and testing. If NULL, it will be fetched using the get_Data_formatted_for_ml_and_best.m function.	DataFrame (optional)
path_db	The path to the database that contains the dataset.	String
rat_studies	A flag indicating whether to use rat studies data. Default is FALSE.	Boolean
studyid_metadata	Metadata related to the study IDs. Default is NULL.	DataFrame (optional)
fake_study	A flag indicating whether to use fake study data. Default is FALSE.	Boolean
use_xpt_file	A flag indicating whether to use an XPT file. Default is FALSE.	Boolean
Round	A flag indicating whether to round the predictions. Default is FALSE.	Boolean
Impute	A flag indicating whether to impute missing values. Default is FALSE.	Boolean
reps	The number of repetitions for the cross-validation process.	Integer
holdback	The proportion of data to hold back for testing during cross-validation.	Numeric
Undersample	A flag indicating whether to perform undersampling on the dataset. Default is FALSE.	Boolean
hyperparameter_tuning	A flag indicating whether to perform hyperparameter tuning. Default is FALSE.	Boolean
error_correction_method	The method to use for error correction (e.g., “Flip”, “Prune”, or “None”).	String
testReps	The number of test repetitions for model evaluation.	Integer

Output

The function returns a histogram plot visualizing the predicted probabilities for the LIVER variable across test repetitions. The plot shows the distribution of predictions (probabilities) for both classes (LIVER = “Y” or “N”).

Key Steps

1. Data Preparation:
   • If Data is NULL, the function fetches and formats the data using the get_Data_formatted_for_ml_and_best.m function.
2. Cross-Validation:
   • The dataset is divided into training and testing sets for each repetition (testReps).
   • If Undersample is enabled, undersampling is applied to balance the dataset.
3. Model Training:
   • A random forest model is trained using the training set for each repetition.
4. Prediction:
   • The model makes predictions on the test set.
   • The predicted probabilities are stored for each repetition.
5. Result Visualization:
   • The predictions are averaged across repetitions, and a histogram is created to visualize the distribution of the predicted probabilities for the LIVER variable.
6. Plot:
   • The histogram is displayed using ggplot2, showing the predicted probabilities for the LIVER outcome (coded as “Y” or “N”).

Example Usage

```r # Example function call get_prediction_plot( path_db = “path_to_db”, rat_studies = FALSE, reps = 10, holdback = 0.2, Undersample = TRUE, hyperparameter_tuning = FALSE, error_correction_method = “Flip”, testReps = 5 )