题目
单项选择题
Mila wishes to build a machine learning model to classify PET images as either containing cancerous tumours, or not. Mila chooses to use decision trees as her base model, and performs the following steps: · First, she splits her data into training/test sets. · Next, Mila creates 100 subsets of her training data by resampling images from the training data, with replacement, until each dataset contains 200 images. · Then, for each subset, she chooses to only look at a random sample of 40 pixels (same pixels for images in the same subset) · Then, she trains 100 models (one for each subset) · Finally, Mila runs each of the 100 models through the test set, and uses as her final prediction for each test image the majority vote of the 100 models. Mila’s experiment is an example of the ensemble method known as:
选项
A.a. Bagging
B.b. Boosting
C.c. Bootstrapping
D.d. Random Forest
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标准答案
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思路分析
The scenario describes an ensemble approach built from multiple decision trees with bootstrap resampling and random feature subsets, followed by majority voting on test predictions.
Option a: Bagging. This term refers to bootstrap aggregating applied to base learners (often decision trees) where each learner is trained on a bootstrap sample and predictions are aggregated, typically by majority vote or averaging. However, ......Login to view full explanation登录即可查看完整答案
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类似问题
Your bank wants to build a system that automatically approves or denies small consumer loans. Analysts have a historical dataset with applicant age, income, marital status, and number of dependents, along with a labeled outcome (“good” vs. “bad” borrower). The team is considering using either a single decision tree or a random forest, and they show you the decision tree diagram below as an example of how the logic works. Which model should the bank choose if they want higher accuracy and more stability in predictions, especially when detecting risky or fraudulent applications?
Mila wishes to build a machine learning model to classify PET images as either containing cancerous tumours, or not. Mila chooses to use decision trees as her base model, and performs the following steps: · First, she splits her data into training/test sets. · Next, Mila creates 100 subsets of her training data by resampling images from the training data, with replacement, until each dataset contains 200 images. · Then, for each subset, she chooses to only look at a random sample of 40 pixels (same pixels for images in the same subset) · Then, she trains 100 models (one for each subset) · Finally, Mila runs each of the 100 models through the test set, and uses as her final prediction for each test image the majority vote of the 100 models. Mila’s experiment is an example of the ensemble method known as:
Mila wishes to build a machine learning model to classify PET images as either containing cancerous tumours, or not. Mila chooses to use decision trees as her base model, and performs the following steps: · First, she splits her data into training/test sets. · Next, Mila creates 100 subsets of her training data by resampling images from the training data, with replacement, until each dataset contains 200 images. · Then, for each subset, she chooses to only look at a random sample of 40 pixels (same pixels for images in the same subset) · Then, she trains 100 models (one for each subset) · Finally, Mila runs each of the 100 models through the test set, and uses as her final prediction for each test image the majority vote of the 100 models. Mila’s experiment is an example of the ensemble method known as:
Mila wishes to build a machine learning model to classify PET images as either containing cancerous tumours, or not. Mila chooses to use decision trees as her base model, and performs the following steps: · First, she splits her data into training/test sets. · Next, Mila creates 100 subsets of her training data by resampling images from the training data, with replacement, until each dataset contains 200 images. · Then, for each subset, she chooses to only look at a random sample of 40 pixels (same pixels for images in the same subset) · Then, she trains 100 models (one for each subset) · Finally, Mila runs each of the 100 models through the test set, and uses as her final prediction for each test image the majority vote of the 100 models. Mila’s experiment is an example of the ensemble method known as:
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