AI-900: What Type of Machine Learning Model Should You Use to Predict Taxi Prices?

Learn why supervised learning is the best approach for building a machine learning model to determine local taxi prices at specific times using historic taxi service data.

Question

You are building a machine learning model to determine a local taxi price at a specific time of a day using historic data from a taxi service database. Which type of machine learning model should you build?

A. Reinforcement Learning
B. Semi-supervised Learning
C. Unsupervised Learning
D. Supervised Learning

Answer

When building a machine learning model to predict local taxi prices at specific times of the day based on historic data from a taxi service database, supervised learning is the most appropriate approach.

D. Supervised Learning

Explanation

Supervised learning involves training a model on a labeled dataset, where the correct output (in this case, the taxi price) is known for each input data point (the time of day and other relevant features). The model learns to map the input features to the correct output labels. Once trained, the model can then predict taxi prices for new, unseen data points.

This scenario is well-suited for supervised learning because:

1. You have a clear input (time of day and other relevant features) and a known output (taxi price) that you want to predict.
2. You have historic data from the taxi service database, which can serve as a labeled training dataset for the model.
3. The goal is to learn a mapping function from the input features to the output price, which is the core objective of supervised learning algorithms.

Other learning approaches are less suitable:

• Unsupervised learning finds patterns in unlabeled data, which is not the case here since you have labeled taxi prices.
• Semi-supervised learning uses a mix of labeled and unlabeled data, but here your historic dataset is fully labeled.
• Reinforcement learning trains agents to take actions in an environment to maximize a reward, which does not fit this price prediction task.

Therefore, building a supervised learning model is the best approach for predicting taxi prices based on historic data and specific time-of-day inputs.

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