KNIME_​ML_​Project

<p><strong>Data Strategy and Balancing </strong></p><ul><li><p><strong>Objective</strong>: Optimize the dataset for machine learning modeling through feature normalization, class balancing, and data partitioning.</p></li><li><p><strong>Internal Nodes and Parameters</strong>:</p><ul><li><p><strong>Normalizer</strong>: Applied the <strong>Min-Max (0-1)</strong> method to all numerical features to ensure equal weighting during model training.</p></li><li><p><strong>SMOTE</strong>: Performed oversampling on the minority class "<strong>Biopsy</strong>" (K=5) to achieve a perfect <strong>50/50 balance</strong>, resulting in <strong>803 records per class</strong>.</p></li><li><p><strong>Table Partitioner</strong>: Executed a <strong>70% Training / 30% Test</strong> split using <strong>stratified sampling</strong> to maintain class proportions across both datasets.</p></li></ul></li><li><p><strong>Assumptions and Missingness</strong>:</p><ul><li><p>The input data is assumed to be free of missing values, as imputation was finalized during the previous phase.</p></li><li><p>The target variable (Biopsy) was converted to <strong>String</strong> format to satisfy the algorithmic requirements of the SMOTE node.</p></li></ul></li></ul>