Python Workshop Day- 4

What is simple linear regression used for?

• Classifying data into multiple categories

• Handling outliers in a dataset

• Predicting a continuous output variable based on one input feature

• Dimensionality reduction of data

In the context of linear regression, what does the "best fit line" refer to?

• A line that perfectly fits all data points

• A line that minimizes the errors between predicted and actual values

• A line with the steepest slope

•  A line that ignores outliers in the dataset

Which mathematical technique is commonly used to find the best fit line in linear regression?

• Gradient descent

• Matrix factorization

• Principal Component Analysis (PCA)

• Singular Value Decomposition (SVD)

What is the purpose of a cost function in machine learning?

• To maximize the accuracy of the model

• To add complexity to the model

• To improve generalization

• To minimize the difference between predicted and actual values

What does the gradient in gradient descent represent?

• The direction of steepest ascent

• The step size for each iteration

• The rate of convergence

• The learning rate

In a simple linear regression equation Y = mx + c, what does 'c' represent?

• Intercept

• Coefficient

• Gradient

• Slope

What is the primary goal of gradient descent in machine learning?

• Maximizing the accuracy of the model

• Minimizing the loss function

• Avoiding overfitting

• Regularizing the model

What is the purpose of the learning rate in gradient descent?

• Controlling the rate of convergence

• Controlling the number of iterations

• Determining the number of features

• Modifying the number of samples

For a simple linear regression model with a slope of 2.5 and an intercept of 3.2, what is the predicted value of the dependent variable (Y) when the independent variable (X) is 6?

• 18.7

• 15.2

•  16.0

• 18.2

 If the slope of a simple linear regression model is 1.8 and the intercept is -3.2, calculate the predicted value of the dependent variable (Y) when the independent variable (X) is 9.

• 13.6

• 16.4

• 14.6

• 12.4