Learn the correct modules to use in Azure Machine Learning Designer for building a model that predicts automobile prices. Discover how to select columns, split data, and apply linear regression for accurate price predictions.
Table of Contents
Question
You are using Azure Machine Learning designer to build a model that will predict automobile prices. Which type of modules should you use to complete the model?
A. 1 – Split Data, 2 – Summarize Data, 3 – Linear Regression
B. 1 – Split Data, 2 – Convert to CVS, 3 – K-Means Clustering
C. 1 – Select columns in Dataset, 2 – Split Data, 3 – Linear Regression
Answer
C. 1 – Select columns in Dataset, 2 – Split Data, 3 – Linear Regression
Explanation
To build a model in Azure Machine Learning Designer that predicts automobile prices, you should use the following modules in the given order:
- Select columns in Dataset: This module allows you to choose the relevant features (columns) from your dataset that will be used for training the model. By selecting only the necessary columns, you can focus on the most important variables that impact automobile prices.
- Split Data: After selecting the relevant columns, you need to split your dataset into training and testing subsets. The Split Data module helps you divide your data into two separate datasets – one for training the model and the other for evaluating its performance. This is a crucial step in the machine learning process to ensure your model generalizes well to unseen data.
- Linear Regression: Once you have prepared your data, you can use the Linear Regression module to train your predictive model. Linear regression is a supervised learning algorithm that attempts to establish a linear relationship between the input features (selected columns) and the target variable (automobile prices). By fitting a linear equation to the training data, the model learns to make price predictions based on the input features.
Using these three modules in the specified order will allow you to build a machine learning model capable of predicting automobile prices based on the selected features. The Select columns in Dataset module ensures you are using the most relevant data, the Split Data module helps you create separate datasets for training and testing, and the Linear Regression module trains the model to make accurate price predictions.
Microsoft Azure AI Fundamentals AI-900 certification exam practice question and answer (Q&A) dump with detail explanation and reference available free, helpful to pass the Microsoft Azure AI Fundamentals AI-900 exam and earn Microsoft Azure AI Fundamentals AI-900 certification.