Fundamentals of Machine Learning - Linear Regression - Labs

The video tutorial covers linear regression using the auto MPG dataset. It explains how to prepare the data, build a simple linear regression model using horsepower, and evaluate the model's performance. The tutorial also demonstrates data visualization techniques to understand the model's predictions and the relationship between variables.

What is the primary goal of the linear regression problem discussed in the video?

• To identify the origin of cars

• To classify different types of cars

• To determine the price of cars

• To predict the fuel efficiency of automobiles

Which library is NOT mentioned as necessary for the linear regression task?

• NumPy

• Pandas

• Scikit-learn

• TensorFlow

What method is used to handle missing values in the dataset?

• Interpolate missing values

• Fill with zeros

• Drop rows with missing values

• Replace with mean

How are categorical features like 'Origin' processed for machine learning?

• Using binary encoding

• Using ordinal encoding

• Using one-hot encoding

• Using label encoding

What is the purpose of splitting the dataset into training and testing sets?

• To increase the complexity of the model

• To simplify the data processing

• To ensure the model is not overfitting

• To reduce the size of the dataset

Which visualization technique is used to understand the relationships between features?

• Heatmap

• Pair plot

• Line graph

• Bar chart

What is the main reason for normalizing the data before building the model?

• To make the data more complex

• To ensure all features are on a similar scale

• To increase the number of features

• To reduce the number of samples

Which API is used to build the linear regression model in the video?

• Scikit-learn

• Theano

• PyTorch

• TensorFlow Keras

What does the 'mean absolute error' indicate in the context of model evaluation?

• The variance of the model's predictions

• The average distance between predicted and actual values

• The total number of errors in the model

• The percentage of correct predictions

What is suggested as a next step to improve the linear regression model?

• Use a different dataset

• Add more features to the model

• Decrease the number of epochs

• Remove the normalization step