Model Training and Evaluation Concepts

This video tutorial explains the importance of splitting a dataset into training and test sets using the train-test split method. It uses a BMW car prices dataset to demonstrate data visualization and the implementation of the train-test split using SKLearn. The tutorial covers setting a random state for consistent data splits and training a linear regression model. It concludes with a summary and additional resources for further practice.

Why is it not a good strategy to train a model using the entire dataset?

• It requires more data storage.

• It is too expensive.

• It can lead to overfitting.

• It takes too long to process.

What are the independent variables in the BMW car prices dataset?

• Year of manufacture

• Mileage and age of the car

• Selling price

• Brand and model

What is the dependent variable in the BMW car prices dataset?

• Mileage

• Age of the car

• Selling price

• Brand

What visualization library is used to plot the data in the tutorial?

• ggplot

• plotly

• seaborn

• matplotlib

Which library provides the train-test split method used in the tutorial?

• matplotlib

• pandas

• numpy

• sklearn

What percentage of the dataset is used for testing in the tutorial?

• 20%

• 40%

• 10%

• 30%

What is the result of using the train-test split method?

• Two datasets: one for training and one for testing

• A dataset with missing values

• A single dataset with shuffled data

• A dataset with normalized values

What is the purpose of using a random state in sampling?

• To improve the accuracy of the model

• To decrease the processing time

• To ensure the same samples are selected each time

• To increase the size of the dataset

What method is used to train the linear regression model?

• fit

• train

• run

• execute

What is the accuracy of the linear regression model as mentioned in the tutorial?

• 1.00%

• 0.95%

• 0.89%

• 0.75%