# Radiomics for LGG dataset — Data

来源：https://www.kaggle.com/competitions/glioma-radiomics/data

本地 Meta Kaggle 快照；不是本次实时页面。

# Dataset Description


<h2>File descriptions</h2>

The task for this competition is to predict mutation for the test set, using models trained on the training set. Both test and training set have 640 radiomic features (columns in the CSV). 
<ul>
<li><strong>train.csv</strong> - training set, consisting of 105 samples. The first column is the ID for the sample. There are 640 columns corresponding to 640 radiomic features. The last column is the ground truth (binary value for Mutacion)</li>
<li><strong>test.csv</strong> -test set, consisting of 44 samples. The first column is the ID of the patient. No ground truth is informed. To win the task you should obtain the highest accuracy for prediction of the Mutacion binary value in these test samples. </li>
<li><strong>example-submission.csv</strong> example of a solution file, that should contain 44 rows corresponding to the 44 test samples, and two columns per row: the patient ID as it appears in test.csv and its predicted Mutation value (0 or 1)