{"id":47419,"date":"2024-04-15T22:26:29","date_gmt":"2024-04-15T22:26:29","guid":{"rendered":"https:\/\/exam.pscnotes.com\/mcq\/?p=47419"},"modified":"2024-04-15T22:26:29","modified_gmt":"2024-04-15T22:26:29","slug":"which-of-the-following-can-be-true-for-selecting-base-learners-for-an-ensemble-1-different-learners-can-come-from-same-algorithm-with-different-hyper-parameters-2-different-learners-can-come-from-d","status":"publish","type":"post","link":"https:\/\/exam.pscnotes.com\/mcq\/which-of-the-following-can-be-true-for-selecting-base-learners-for-an-ensemble-1-different-learners-can-come-from-same-algorithm-with-different-hyper-parameters-2-different-learners-can-come-from-d\/","title":{"rendered":"Which of the following can be true for selecting base learners for an ensemble? 1. Different learners can come from same algorithm with different hyper parameters 2. Different learners can come from different algorithms 3. Different learners can come from different training spaces"},"content":{"rendered":"

[amp_mcq option1=”1″ option2=”2″ option3=”1 and 3″ option4=”1, 2 and 3″ correct=”option4″]<\/p>\n

The correct answer is D. 1, 2 and 3.<\/p>\n

An ensemble model is a type of machine learning model that combines the predictions of multiple models to produce a more accurate prediction. The base learners in an ensemble model can be selected from the same algorithm with different hyperparameters, from different algorithms, or from different training spaces.<\/p>\n

Selecting base learners from the same algorithm with different hyperparameters can help to improve the accuracy of the ensemble model by reducing overfitting. Overfitting occurs when a model learns the training data too well and is not able to generalize to new data. By using different hyperparameters, the base learners in the ensemble model will learn different aspects of the training data, which can help to reduce overfitting.<\/p>\n

Selecting base learners from different algorithms can also help to improve the accuracy of the ensemble model. Different algorithms have different strengths and weaknesses, so by combining the predictions of multiple algorithms, the ensemble model can benefit from the strengths of each algorithm.<\/p>\n

Selecting base learners from different training spaces can also help to improve the accuracy of the ensemble model. Different training spaces can contain different types of data, so by combining the predictions of multiple models trained on different data, the ensemble model can benefit from the different types of data.<\/p>\n

In conclusion, the correct answer is D. 1, 2 and 3. All of the options can be true for selecting base learners for an ensemble model.<\/p>\n","protected":false},"excerpt":{"rendered":"

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