You are provided with data about Airbnb listings and you want to predict the price for a new listing based on its descriptive features such as number of bedrooms, and its location. The task is:

Question

BlackTom AI · Accepted Answer

Predicting a continuous numeric target (price) requires regression.

1. Relevant knowledge points and concepts
- Supervised learning: uses labeled examples (features + known target) to learn a mapping.
- Regression vs classification: regression predicts a continuous numeric variable; classification predicts discrete class labels.
- Common regression methods: linear regression, decision tree regression, random forest regression, gradient boosting.
- Model pipeline: feature encoding (e.g., bedrooms numeric, location one-hot or embeddings), training to minimize a loss (e.g., mean squared error), and prediction.

2. Step-by-step reasoning process
- The target to predict is "price" which is a continuous numeric value (dollars). That places the problem in the regression family of supervised learning.
- The inputs (number of bedrooms, location, etc.) are features used to estimate that numeric target.
- During training, the model learns parameters so that predicted prices are close to actual prices on training data; during inference it outputs a numeric price for a new listing.

3. Example calculation with intermediate steps
- Use a simple linear regression model: price = β0 + β1*(bedrooms) + β2*(location_score).
- Suppose after training we obtain β0 = 50, β1 = 30, β2 = 10.
- For a new listing with 2 bedrooms and location_score = 3:
  - compute contribution: β1*(bedrooms) = 30 * 2 = 60
  - compute location contribution: β2*(location_score) = 10 * 3 = 30
  - add intercept: price = 50 + 60 + 30 = 140 (currency units)
- This demonstrates predicting a continuous numeric value, characteristic of regression.

4. Why the correct answer is correct
- Because price is continuous and we estimate a numeric value from features, regression is the appropriate task type.

5. Why the other options are incorrect
- Classification: targets discrete labels (e.g., "cheap" vs "expensive"), not a continuous price.
- Dimensionality reduction: reduces feature dimensionality (e.g., PCA) but does not directly predict price.
- Clustering: unsupervised grouping of similar listings, no supervised numeric prediction.

6. Conclusion
The correct task type is Regression, which matches the provided answer.

You are provided with data about Airbnb listings and you want to predict the price for a new listing based on its descriptive features such as number of bedrooms, and its location. The task is:单项选择题

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