Python In Practice - 15 Projects to Master Python - Asking the Model to Make Predictions - Machine Learning with Python

Python In Practice - 15 Projects to Master Python - Asking the Model to Make Predictions - Machine Learning with Python

Assessment

Interactive Video

Information Technology (IT), Architecture

University

Practice Problem

Hard

Created by

Wayground Content

FREE Resource

The video tutorial explains how to create a machine learning model using linear regression in Python. It covers the process of initializing the model, training it with input and output data using the fit method, and making predictions with new data. The tutorial also discusses evaluating the model's performance by splitting data into training and testing sets to compare predicted and actual values.

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7 questions

Show all answers

1.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the primary algorithm used in the model discussed in the video?

Linear Regression

Neural Network

Support Vector Machine

Decision Tree

2.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Which method is used to train the model in the video?

fit

train

execute

learn

3.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the correct format for input data when making predictions?

A single value

A 1D array

A 2D array

A dictionary

4.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the predicted weight for a height of 60 inches according to the model?

105 pounds

120 pounds

99 pounds

112 pounds

5.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Why is it important to split data into training and testing sets?

To evaluate the model's performance

To improve the speed of the model

To increase the accuracy of predictions

To reduce the size of the dataset

6.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

How many samples are used for training in the example provided?

100 samples

180 samples

200 samples

150 samples

7.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the purpose of comparing predicted values with actual values?

To change the algorithm used

To adjust the model's parameters

To determine the model's accuracy

To increase the dataset size

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