Random Forest Regression Introduction and Intuition

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About this video:
This video titled "Random Forest Regression Introduction and Intuition" explains the ensemble learning method, its various techniques like boosting and bagging. It also explains which approach random forest algorithm takes to develop the random forest regression model. Then later on it covers advantages and disadvantages associated with Random Forest Regression algorithm.

Subtitles available in: English

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#RandomForestRegression #EnsembleLearning #MachineLearning

[TheAIUniversity]

Trivia question from the video: State True or False:
Decision trees are computationally expensive?

[dr.cancan]

Good video, thanks! However, I think you missed talking about bootstrapping procedure in the slide of "What is random forest regression?". A bootstrap sample is a random sample of the data taken with replacement. Meaning that after a sample is selected for inclusion in the subset, it’s still going to be available for further selection. Actually, on average, ≈ 63.21 % of the original samples end up in any particular bootstrap sample. Then, also randomly features/ predictor variables are selected to build the decision trees.

[shawnstein]

Hello, are model tree ensemble and random forest the exact same thing or is random forest a type of model tree ensemble?

[mcbhuva]

Yes decision Tree is computationally expensive

[jayasinha]

random-forest Regression tho batya hi nahi