# A-Tuning Ensemble Machine Learning Technique for Cerebral Stroke Prediction
## 4 Implementation and Evaluation
### Tuning Parameter Using Random Search Optimization
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Meaning** & **Best Value** \\ \hline bootstrap & Bootstrapping generates simulated datasets by resampling the original dataset with replacement many thousands of times. & True \\ \hline ccp alpha & Minimal cost-complexity pruning (ccp) employs a complexity parameter. & 0.0 \\ \hline class weight & Weights that are related to classes. & None \\ \hline criterion & The capability to assess a split’s quality. & gini \\ \hline Max depth & The leaf and root nodes are measured by the maximum number of levels in the tree. & None \\ \hline Max features & The aspect number must be taken into consideration when selecting the best data split. & Auto \\ \hline Max leaf nodes & The trees are grown using the maximum nodes of the leaf. & None \\ \hline Max samples & How many samples from X need to be taken to train each base estimator & None \\ \hline Min impurity decrease & A split node will experience an impurity reduction greater than or equal this amount. & 0.0 \\ \hline Min samples leaf & The smallest number of required samples at each leaf node. & 1 \\ \hline Min samples split & This setting instructs the decision tree in a random forest to divide any node with less observation than necessary. & 2 \\ \hline Min weight fraction leaf & A leaf node must contain a minimum weighted percentage of the total weights from all of the input samples. & 0.0 \\ \hline N estimators & This parameter refers to the number of trees required to be built before taking the maximum voting or averages of predictions. & 100 \\ \hline Random state & Random number seed. & 123 \\ \hline N jobs & The number of concurrent jobs to run & \(-1\) \\ \hline Oob score & Score from an out-of-bag estimate of the training dataset. & False \\ \hline verbose & Regulates the amount of jargon used in fitting and predicting. & 0 \\ \hline Warm start & If True is set, use the previous fit call’s solution and add more estimators to the ensemble; if not, just fit a new forest. & False \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Tuned parameters for RF. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Meaning** & **Best Value** \\ \hline Objective & Output probability, logistic regression for binary classification. & binary:logistic \\ \hline Base score & Global bias, the overall initial prediction score. & 0.5 \\ \hline booster & Gradient boosting methods. & gbtree \\ \hline Colsample bylevel & Ratio of columns in the subsample for each level. & 1 \\ \hline Colsample bynode & The column-to-node subsample ratio (split). & 1 \\ \hline Colsample bytree & Column subsample ratio used to construct each tree. & 1 \\ \hline Learning rate & The learning rate of boosting. & 0.300000012 \\ \hline Max bin & The maximum number of discrete bins can be used to group & 256 \\ \hline gamma & The least loss reduction is needed for a subsequent part on a tree leaf node. & 0 \\ \hline Max cat to onehot & A threshold for determining whether categorical data should be split using a one-hot encoding-based split in XGBoost. & 4 \\ \hline Max delta step & Utilized to safeguard optimization. & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Tuned parameters for XGBoost.