A hospital has a dataset of past patients. For each patient, the dataset includes age, blood pressure, cholesterol level, smoking status, and whether the patient was later diagnosed with heat disease. The hospital wants to train a model that can predict whether a new patient is likely to have heart disease.

Which type of machine leaning should be used?

Question

A hospital has a dataset of past patients. For each patient, the dataset includes age, blood pressure, cholesterol level, smoking status, and whether the patient was later diagnosed with heat disease. The hospital wants to train a model that can predict whether a new patient is likely to have heart disease.

Which type of machine leaning should be used?

BlackTom AI · Accepted Answer

Because the dataset contains both input features (age, blood pressure, cholesterol, smoking status) and the known outcome (whether each patient was later diagnosed with heart disease), this is a labelled learning problem and therefore supervised learning is the correct approach.

Relevant knowledge points and concepts:
- Supervised learning uses labelled examples (features + known target) to learn a mapping from inputs to outputs. It can be used for classification (discrete labels) or regression (continuous targets).
- Unsupervised learning works with unlabelled data to discover structure (clustering, dimensionality reduction) and is not intended to predict a known outcome.
- Predicting "diagnosed with heart disease" is a binary classification task (yes/no).

Step-by-step reasoning:
1. Identify inputs and target: Inputs = age, blood pressure, cholesterol, smoking status. Target = diagnosed with heart disease (yes/no). The presence of the target label for every example signals labelled data.
2. Choose the paradigm: Labelled data → supervised learning. Because the target is categorical (two classes), the specific subtask is binary classification.
3. Select algorithms (examples): logistic regression, decision trees, random forests, support vector machines, or neural networks can be trained on the labelled dataset.
4. Train and evaluate: Fit model on training data, tune hyperparameters (cross-validation), and evaluate using classification metrics (accuracy, precision, recall, F1, ROC-AUC), especially checking for class imbalance.

Why the correct answer is correct:
- The provided dataset explicitly includes the correct outcome for each patient, which is the defining requirement for supervised learning. The task is to learn the relationship between features and that outcome to make predictions on new patients.

Why other options are incorrect:
- Options claiming unsupervised learning are wrong because unsupervised methods do not use labelled outcomes and are intended for pattern discovery, not prediction of a known label.
- The first incorrect supervised option reverses the logic by saying "model does not need examples with answers," which contradicts the definition of supervised learning.

Conclusion: The correct choice is "Supervised learning, because the dataset includes the corect outcome for each patient." (matches the provided answer).

类似问题

Which of the following is a supervised learning algorithm?

In supervised learning, what is true about the dataset?

Question16 What is the best approach to solve this problem:Given a website visiting activity dataset with a record of confirmed buyers, find whether a new visiter is a casual browser or a serious buyer. Regression Clustering Classification ResetMaximum marks: 0.5 Flag question undefined

Which type of machine learning typically requires more human involvement in training the system to produce useful results?

MULTIPLE ANSWERS: In the Google Teachable Machine Supervised Machine Learning exercise, you learned the following:

哪些分类算法是监督学习算法？多选题 Which classification algorithms are supervised learning algorithms? Select all that apply.

A model is trained to predict missing values in a dataset using other features as input. 👉 What type is this?

A dataset contains customer transactions with no labels. A team manually defines “high-value customers” using rules, then trains a model to predict this label. 👉 What type of learning is this?

更多留学生实用工具

智能学习助手

风格化写作助手

论文查重助手

文献引用助手

课堂转译助手

课堂笔记助手

Quiz搜索助手

学校历年真题

智能刷题助手

智能匹配练习

希望你的学习变得更简单