Introduction to Machine Learning (Gradient Descendent)

As the number of training examples goes to infinity, your model trained on that data will have:

• Lower variance

• Higher variance

• Same variance

• None of the above

Consider the following training set of m=4 training examples: (x,y)={ (1, 0.5), ( 2, 1) , (4, 2), ( 0,0)}. Consider the linear regression model h_θ(x)=θ₀+θ₁x. What are the values of θ₀ and θ₁ that you would expect?

• θ₀=0.5,θ₁=0

• θ₀=0.5,θ₁=0.5

• θ₀=1,θ₁=0.5

• θ₀=0,θ₁=0.5

Suppose we set θ₀=−1,θ₁=0.5. What is h_θ(4)?

• 0.5

• -1

• 1

• 0

Predicting the amount of rainfall in a region based on various cues is a ______ problem.

• Supervised learning

• Unsupervised learning

• Clustering

• None of the above

You trained a binary classifier model which gives very high accuracy on the training data, but much lower accuracy on validation data. Which of the following may be true?

• This is an instance of overfitting.

• This is an instance of underfitting.

• The training was not well regularized.

• The training and testing examples are sampled from different distributions.

How many coefficients do you need to estimate in a simple linear regression model (One independent variable)?

• One

• Two

• Three

• Four

________ is a simple approach to supervised learning. It assumes that the dependence of Y on X1, X2, . . . Xp is linear.

• Linear regression

• Logistic regression

• Gradient Descent

• All of these

Every ________ function has a probability distribution function.

• Continuous

• Discrete

• Categorical

• Random