What is the difference between Random Forest and Gradient Boosting alg

1.	What is the difference between Random Forest and Gradient Boosting algorithms? Explain briefly.
Answer» Both Random Forest (RF) and Gradient BOOSTING (GBM) are tree-based supervised machine learning ALGORITHMS. Both use a tree-based MODELING approach and ENSEMBLE methods are used. RF uses DECISION trees, kind of complex form of a tree-based algorithm, which is inclined to overfitting. GBM instead is a boosting-based algorithm approach, which is based on weak classifiers. Accuracy of RF can be manipulated by modifying variance. GBM will have more hyper-parameters to tune for accuracy and can be planned to play for a tradeoff between bias and variance.

What is the difference between Random Forest and Gradient Boosting algorithms? Explain briefly.

Answer»

Both Random Forest (RF) and Gradient BOOSTING (GBM) are tree-based supervised machine learning ALGORITHMS. Both use a tree-based MODELING approach and ENSEMBLE methods are used.

RF uses DECISION trees, kind of complex form of a tree-based algorithm, which is inclined to overfitting. GBM instead is a boosting-based algorithm approach, which is based on weak classifiers.

Accuracy of RF can be manipulated by modifying variance. GBM will have more hyper-parameters to tune for accuracy and can be planned to play for a tradeoff between bias and variance.

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