Linear algebra and calculus

What does the derivative of a function represent in data analysis?

• The function's maximum value
  

• The function's minimum value
  

• The rate of change of the function
  

• The total area under the curve

What does a zero derivative (gradient) indicate during model training?

• The data is missing
  

• The model accuracy is 100%
  

• A possible local minimum or plateau
  

• The learning rate is too high

If the derivative of a function is always positive, what does that imply about the function?

• It’s constant
  

• It’s decreasing
  

• It’s increasing
  

• It’s at a minimum

What is the goal of gradient descent in machine learning?

• To find the largest output value
  

• To minimize the loss function
  

• To reduce the dataset size
  

• To normalize the input features

The derivative of a model’s output with respect to an input feature shows:

• The training time
  

• The importance or sensitivity of that input
  

• The memory usage
  

• The number of layers in the model

If a small change in an input causes a large change in prediction, what can we infer?

• The model is under fitting
  

• The input is not relevant
  

• The model is sensitive to that input
  

• The output is always constant

In a regression model, the partial derivative of the output with respect to an input feature is close to zero. What does this indicate?

• The feature has a strong nonlinear effect
  

• The feature has a strong linear effect
  

• The output is insensitive to the feature
  

• The model is overfitting

• Linear

• nonlinear

• quadratic

• biquadratic

the data function y=sinx is nonlinear

• True
• False

The slope of the data function y=exp(x) is exp(2x)

• True
• False