Anomaly Detection Using Explainable Random Forest for the Prediction of Undesirable Events in Oil Wells

<table class="table-group" id="tab3"><tr><td><table class="table"><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr class="thead"><td class="align_left">Hyper-parameter</td><td class="align_center">Value</td></tr><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr><td class="align_left">Criterion</td><td class="align_center">Entropy</td></tr><tr><td class="align_left">max_depth</td><td class="align_center">8</td></tr><tr><td class="align_left">max_features</td><td class="align_center">Auto</td></tr><tr><td class="align_left">n_estimators</td><td class="align_center">30</td></tr><tr><td class="align_left">Random state</td><td class="align_center">3</td></tr><tr class="table-tr"><td colspan="2"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>Optimal hyper-parameters of random forest.</div>

Applied Computational Intelligence and Soft Computing

tab3

Table 3

Table 3: Anomaly Detection Using Explainable Random Forest for the Prediction of Undesirable Events in Oil Wells 