learning.md

description
Our software provides tools for model training using extracted features and facilitates the analysis of machine learning results, with a focus on identifying the optimal feature types.

👨‍🏫 Learning

{% hint style="success" %} A tutorial video is available at the bottom of the page {% endhint %}

Like feature extraction, the learning module also utilizes a drag-and-drop design, allowing users to define their machine learning pipeline which incorporates feature processing steps such as cleaning, feature selection, and more.

The next sections explain how to use this module to train your models and analyze results.

The following image depicts the available resources in the leaning module interface:

Having gained an understanding of the various components of the extraction interface, the following section illustrates the available nodes, highlighting their individual functionalities, inputs, and outputs:

Node	Description	In-Nodes	Out-Nodes
	Based on the chosen options, this node generates and stores a holdout set for subsequent model evaluation.	No inputs	Design
	The design node allows you to configure various options for your machine learning experiment, including the experiment name, data splitting method, train and test portions, and more. Additionally, this node will create a separate folder to store all files related to the experiment.	Split	Data
	This is employed to specify your feature set. After setting the features folder, you can choose the features file (CSV files) to use in your experiment.	Design	Cleaning Normalization Feature Reduction Radiomics Learner
	Data cleaning consists of four parts: Remove missing features Remove missing patients Remove invariant features Impute missing features	Data	Normalization Feature Reduction Radiomics Learner
	Normalizing radiomics features using ComBat method to mitigate batch effects arising from data from different sources.	Data Cleaning	Feature Reduction Radiomics Learner
	Feature selection consists of selecting predictive features using the false discovery avoidance method. All the parameters are explained in the method's paper.	Data Cleaning Normalization	Radiomics Learner
	This node sets all options to the model used. XGBoost is the only algorithm supported for now, and its settings can be tuned using an automatic approach: PyCaret or using a random or a grid search.	Data Cleaning Normalization Feature Reduction	Analyze
	The final node is used for analyzing the results obtained from the experiment. You can utilize this node to select specific analysis methods, such as histograms, heatmap, or importance tree. Each method comes with its own set of options that need to be configured. For additional information, please refer to the documentation available in MEDimage.	Radiomics Learner	No outputs

Tutorial video

{% embed url="https://www.youtube.com/watch?ab_channel=MEDomicsLab&v=Q4Y5xsvi9G0" %}

A detailed demonstration on renal cell carcinoma dataset can be found in the next page.