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Can you elucidate the principle of a Random Forest in machine learning?

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Answers

Anonymous

a year ago
3.8Strong
The random forest algorithm is an ensemble method that combines multiple decision trees to make a prediction. It is very flexible and can be applied to a large number of tasks. It functions by creating a random decision tree and fitting it to the data, typically with a subset of features available. The final prediction is the average (or majority rule) decision. 

This is like having a number of interviews evaluating a candidate. Each one will be interested in a different subset of qualifications, and by averaging them, all the view points are incorporated. 
  • Can you elucidate the principle of a Random Forest in machine learning?
  • How would you describe the Random Forest algorithm to someone unfamiliar with it?
  • Could you give an overview of what Random Forest is and how it works?
  • What is the Random Forest concept, and how does it apply to machine learning?
  • How does the Random Forest technique function in the context of decision-making algorithms?
  • Can you explain the Random Forest methodology and its use in data analysis?
  • Would you mind explaining the Random Forest model and its significance in predictive analytics?
  • What can you tell us about the Random Forest algorithm and its implementation?
  • How does Random Forest contribute to the field of ensemble learning methods?
  • In simple terms, what does the Random Forest algorithm do, and why is it useful?
  • Explain the concept of Random Forest.
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Interview question asked to Machine Learning Engineers interviewing at Samsung, Airbnb, Citrix and others: Can you elucidate the principle of a Random Forest in machine learning?.