Fundamentals of Neural Networks - Linear Regression

The video tutorial introduces statistical machine learning, focusing on linear regression. It explains the linear regression model, its components, and the ordinary least squares (OLS) method for optimization. The tutorial discusses the importance of continuous Y values and the challenges posed by discrete Y values, leading to an introduction to logistic regression for classification tasks.

What is the primary purpose of a linear regression model?

• To cluster data points into groups

• To classify data into categories

• To predict a target as a weighted sum of feature inputs

• To reduce the dimensionality of data

Which of the following is NOT a term used to describe the inputs in a linear regression model?

• Variables

• Covariates

• Classes

• Features

In the context of linear regression, what is the role of the epsilon term?

• It is the intercept of the regression model

• It is a constant that scales the features

• It represents the slope of the regression line

• It accounts for the error that cannot be modeled

What does the Ordinary Least Squares (OLS) method aim to minimize?

• The number of features in the model

• The variance of the model

• The mean square error

• The sum of absolute errors

Why is it important for the difference between the predicted and actual values to be small in linear regression?

• To improve the accuracy of predictions

• To ensure the model is overfitting

• To increase the complexity of the model

• To make the model more interpretable

What challenge does a discrete target variable pose for linear regression?

• It does not map well to a real line

• It leads to overfitting

• It makes the model more complex

• It increases the variance of the model

What is the main reason for introducing logistic regression?

• To improve the speed of model training

• To reduce the number of features in a dataset

• To handle continuous target variables

• To address the limitations of linear regression with discrete targets