Done

Done

I learned in this video, Random Forest.

I learned in this lecture, Random Forest Theory.

Random Forest algorithm comes in the Ensemble Method bagging type, which usually applies to large datasets. Further, I learned key features, advantages, applications, best practices, and challenges of the Random Forest algorithm.

AOA, I learned in this lecture how random forests work.1-Bootstrap Aggregation (Bagging) 2-Random Feature Selection ( model more robust and prevents overfitting ) 3-Aggregation of ResultsAnd I also learned key features of Random Forest, which are1-Robustness ( less prone to overfitting ) 2-Handling Non-Linearity ( capable of handling non-linear data ) 3-Feature Importance ( which feature is important to prediction )And I also learned best practices and considerations of RF, which are1-Numbers of tree ( more computational and cost-effective) 2-Depth of tree ( deeper trees can capture more information but might lead to overfitting ) 3-Feature selection ( features are relevant and good quality ) ALLAH PAK aap ko sahat o aafiat wali lambi umar ata kray aor ap ko dono jahan ki bhalian naseeb farmaey,Ameen.