Intro to Linear Regression

Which type of question does regression analysis primarily address?

In linear regression, the goal is to fit a curve, rather than a straight line, through the data points.

In the context of predicting house prices using linear regression, what does the 'fitting' process refer to?

In linear regression for predicting house prices, what does the x-axis commonly represent?

Increasing the slope (w₁) in the equation $y = w_1x + w_2$ while keeping $w_2$ constant will make the line steeper.

In the linear equation $y = w_1x + w_2$, the term w_2 represents the ________.

Match the terms with their descriptions in the context of linear regression:

What is the primary goal of linear regression?

Batch gradient descent updates the weights of the model after processing each individual data point.

Explain the difference between stochastic gradient descent and batch gradient descent in linear regression.

Which of the following is an example of a regression task?

In linear regression, the predicted output must be a continuous value.

In linear regression, the variable that is being predicted is known as the ________ variable.

If you are building a linear regression model to predict the selling price of a house based on its size, what represents the independent variable?

The goal in linear regression is to minimize the sum of the errors, so it doesn't matter if some errors are positive and some are negative.

Why is it generally better to use Mean Squared Error (MSE) than simply summing the errors in linear regression?

If you want to compare the errors of batches with different sizes in stochastic gradient descent, what should you do?

In the equation $y = 5x + 100$ that predicts house prices, the number 5 represents the y-intercept.

The equation $y = w_1x_1 + w_2x_2+...+w_nx_n+b$ represents a ________ in linear regression where multiple features/inputs are considered.

What is Fitting Line using gradient descent (MSE)?

If the size of the house is the x-axis & the price of the house is the y-axis, then which of the following is the best explanation?

When is Polynomial Regression used?

What type of machine learning algorithm is Logistic regression?

The goal of Logistical Regression is generally classifying emails as spam vs not spam.

_________ is used to map the predicted values to probabilities.

What does 'e' stand for in this equation: $f(x) = \frac{1}{1+e^{-(x)}}$?

The equation $f(x) = \frac{1}{1+e^{-(x)}}$ is Linear Regression.

The y-intercept is also known as w[number]?

Given the equation $y = w_1x + w_2$, which variable is best described as the Slope?

Regression always answers a classification question.

________ Gradient Descent applies the squared (or absolute) trick at every point in our data one by one, and repeating this process many times.

What is considered the Independent Variable, when trying to predict the size of a house?

Linear Regression does not fall under Supervised Learning.

Trying to predict the price of a house given its size is an example of which task?

In the equations given earlier: $y = w_1x + w_2$ & $y = w_1x_1 + w_2x_2+...+w_nx_n+b$, what does $x$ stand for?

Linear regression analysis cannot be used to predict the value of a variable using another variable.

________ gradient descent calculates the gradient of the error function for the entire dataset before updating the weights.

Is predicting the color of a house a regression task?

Only batch gradient descent uses normalization.

In the equation $y = 2x + 2$, if $x = 5$ what is the result for y?

When finding the MSE (Mean Square Error) is it important to square the error? Is that incorrect? Why or why not?