The machine learning algorithm that is based on the idea of bagging is Random Forest.

Here's a step-by-step explanation:

1. Bagging, or Bootstrap Aggregating, is a technique used to reduce the variance of an estimate. It works by creating multiple subsets of the original data, with replacement, and then using these subsets to get a better prediction.

2. Random Forest is a type of ensemble learning method, where a group of weak models combine to form a powerful model. In Random Forest, a set of decision trees are created.

3. Each of these trees is created from a different sample of rows and at each node, a different sample of features is selected for splitting. Each tree gives a "vote" for that class, and the class with the most votes becomes our model's prediction.

4. Regression, Classification, and Decision Tree are types of machine learning algorithms, but they are not specifically based on the idea of bagging. A Decision Tree could be a base learner in a bagging algorithm, but it is not inherently a bagging algorithm.

So, the answer is Random Forest.

Question

The machine learning algorithm that is based on the idea of bagging is Random Forest.

Here's a step-by-step explanation:

1. Bagging, or Bootstrap Aggregating, is a technique used to reduce the variance of an estimate. It works by creating multiple subsets of the original data, with replacement, and then using these subsets to get a better prediction.

2. Random Forest is a type of ensemble learning method, where a group of weak models combine to form a powerful model. In Random Forest, a set of decision trees are created.

3. Each of these trees is created from a different sample of rows and at each node, a different sample of features is selected for splitting. Each tree gives a "vote" for that class, and the class with the most votes becomes our model's prediction.

4. Regression, Classification, and Decision Tree are types of machine learning algorithms, but they are not specifically based on the idea of bagging. A Decision Tree could be a base learner in a bagging algorithm, but it is not inherently a bagging algorithm.

So, the answer is Random Forest.

Knowee AI · Accepted Answer

The machine learning algorithm that is based on the idea of bagging is Random Forest.

Here's a step-by-step explanation:

1. Bagging, or Bootstrap Aggregating, is a technique used to reduce the variance of an estimate. It works by creating multiple subsets of the original data, with replacement, and then using these subsets to get a better prediction.

2. Random Forest is a type of ensemble learning method, where a group of weak models combine to form a powerful model. In Random Forest, a set of decision trees are created.

3. Each of these trees is created from a different sample of rows and at each node, a different sample of features is selected for splitting. Each tree gives a "vote" for that class, and the class with the most votes becomes our model's prediction.

4. Regression, Classification, and Decision Tree are types of machine learning algorithms, but they are not specifically based on the idea of bagging. A Decision Tree could be a base learner in a bagging algorithm, but it is not inherently a bagging algorithm.

So, the answer is Random Forest.

Which of the following machine learning algorithm is based upon the idea of bagging?Random-forestRegressionClassificationDecision treeSAVE

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