Generative AI Certificate Q&A: What steps would you take to build machine learning system to try and do a better job predicting the weather?

Table of Contents

Question
Answer
Explanation
Reference

Question

You are going to use machine learning to try and do a better job predicting the weather. To start out, you just want to classify two weather events: “rain” or “not rain.”

What steps would you take to build this system?

A. Use a linear regression to show the trend line from “not rain” to “rain.”
B. Input all the labeled weather data and allow the system to create its own clusters based on what it sees in the datA.
C. Find labeled weather data, create a small training set of that data, and that set aside more data for the test set.
D. Use reinforcement learning to allow the machine to create rewards for itself based on how well it predicted the weather.

Answer

C. Find labeled weather data, create a small training set of that data, and that set aside more data for the test set.

Explanation

The correct answer is C. Find labeled weather data, create a small training set of that data, and set aside more data for the test set.

To build a machine learning system to predict the weather, one needs to follow some general steps, such as:

Define the problem and the objective: In this case, the problem is to classify two weather events: “rain” or “not rain”. The objective is to build a model that can accurately predict whether it will rain or not in a given location and time.
Collect and prepare the data: One needs to find labeled weather data, which means data that has both the input features (such as temperature, humidity, pressure, etc.) and the output labels (rain or not rain). The data should be relevant, reliable, and representative of the problem domain. The data should also be cleaned, normalized, and split into a training set and a test set. The training set is used to train the model, while the test set is used to evaluate its performance on unseen data.
Choose and train the model: One needs to select a suitable machine learning algorithm or technique for the problem, such as a decision tree, a logistic regression, a support vector machine, or a neural network. The model should be trained on the training set using an optimization method such as gradient descent or stochastic gradient descent. The model should also be tuned and validated using techniques such as cross-validation or grid search to find the optimal hyperparameters.
Evaluate and deploy the model: One needs to test the model on the test set and measure its performance using metrics such as accuracy, precision, recall, or F1-score. The model should also be compared with other models or baselines to assess its strengths and weaknesses. If the model meets the desired criteria, it can be deployed for real-world use.

The other options are incorrect because they do not describe the steps to build a machine learning system for weather prediction.

A. Use a linear regression to show the trend line from “not rain” to “rain”. This option is not relevant for the problem, as linear regression is a technique for regression problems, not classification problems. Linear regression tries to fit a line that minimizes the squared error between the predicted and actual values of a continuous variable, such as temperature or pressure. It does not try to classify discrete categories, such as rain or not rain.
B. Input all the labeled weather data and allow the system to create its own clusters based on what it sees in the data. This option is also not suitable for the problem, as it describes an unsupervised learning technique called clustering. Clustering tries to group similar data points together based on their features, without using any labels or outputs. It does not try to predict any outcomes or classes, such as rain or not rain.
D. Use reinforcement learning to allow the machine to create rewards for itself based on how well it predicted the weather. This option is also irrelevant for the problem, as it describes a different type of machine learning technique called reinforcement learning. Reinforcement learning tries to learn from its own actions and feedback from the environment, without using any labels or outputs. It does not try to predict any outcomes or classes, such as rain or not rain.

Reference

Generative AI Exam Question and Answer

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