I gained knowledge about Bosting, Stacking (Stacked Generalization), and Bagging (Bootstrap Aggregating) from this video ensemble.

Aj e bari eid ka din ha, aur aj e yeh misaal ... <3

Done

Done

I learned in this video Ensemble (Bagging (Bootstrap Aggregating), Bosting, and Stacking(Stacked Generalization))

I learned in this lecture Ensemble Methods (Bootstrap Aggregating), Bosting, and Stacking(Stacked Generalization).

This lecture contains the new concept of Ensemble Algorithms which is near to best in place of neural networks.

AOA, I learned in this lecture about the ML algorithm of Ensemble Methods and its types Which are1- Bagging ( aggregation) ( parallel tree growing with sub-samples ) 2- Boosting ( sequential tree growth with weighted samples ) 3- Stacking ( Stacked Generalization )And also learned the uses of Ensemble Algorithms which are1-For Accuracy 2-For Stability 3-For Reduced Overfitting I also learned about the applications of Ensemble Algorithms which are1- Finance (for credit scoring and algorithmic trading) 2- Healthcare (for disease prediction and diagnosis) 3- E-commerce (for recommendation systems) 4-Stock market ( prediction )I also learned the disadvantages of the Ensemble Algorithm which are1-Complexity ( computationally expensive and take more time ) 2-Interpretability ( harder to interpret ) 3-Parameter Tuning ( requires careful tuning of parameters )I also learned the advantages of the Ensemble Algorithm which are1-Enhanced accuracy of the model 2-Robust the model 3-Generalized the model 4-Improve the model 5-Versatility ( used for both classification and regression tasks) 6-Easy to use ( requires little tuning of parameters )ALLAH PAK aap ko sahat o aafiat wali lambi umar ata kray aor ap ko dono jahan ki bhalian naseeb farmaey,Ameen.