Intro to ML: The ML Revision Quiz

1.

Multiple Choice

1 min

1 pt

If we predict every observation to be True, what will our model precision be?

100%

0%

The proportion of True values in the dataset

Not enough information

Answer explanation

Think of all the False Positives

2.

Multiple Select

1 min

1 pt

James, Amelia, and George are participating in a machine learning competition. They have to choose an algorithm for their project. Select which of the following algorithms they should consider if they want to use eager learners:

K-nearest neighbours

Decision trees

Neural networks

Linear regression

Answer explanation

Recall that K-nn is a lazy learner. At training time the algorithm simply stores the training data - i.e. no calculations/training occurs. It is not until inference time when the algorithm checks the K nearest points to the unseen datapoint in question. All calculations occur at inference time, hence being called lazy rather than eager.

3.

Multiple Select

1 min

1 pt

Which of the following statements are True:

Performance on the validation set can be used to see if a model is overfitting to the training data

We cannot tell from the training performance alone if a model is overfitting or not

Underfitting implies better generalisation to other datasets

Answer explanation

Underfitting is when the model lacks the capacity to fit the underlying pattern/trend of the data. A model that underfits a training set will perform no better on unseen data.

4.

Multiple Select

1 min

1 pt

Scarlett is working on a machine learning project and she is worried about underfitting. Which of the following actions may cause underfitting in her model?

Reducing the max. depth of a decision tree

Increasing the value of K in K-nn

Adding more layers to a neural network

Increasing the size of the training data

Increasing the value of K in K-means

Answer explanation

Underfitting is caused when the model lacks the capacity to fit the underlying trend/pattern of the data.

5.

Multiple Choice

1 min

1 pt

True or False:

If we use grid-search for testing different hyper-parameter values, we can use each of these results for finding the confidence interval of the model error.

True

False

Answer explanation

Confidence Intervals should be reported for the final model architecture. They are a prediction on how the final model will perform on unseen data. If this is calculated with a range of different models, this will clearly be an inaccurate prediction.

6.

Multiple Select

1 min

1 pt

Which of the following algorithms will change given different random seeds:

Neural networks

K-nearest neighbours (K = 1, with no ties)

Decision trees

K-means

Evolution Algorithms using simple tournament

Answer explanation

Think about which methods are deterministic.

7.

Multiple Select

1 min

1 pt

Which statements below are True describing the differences between Gradient Descent, Stochastic Gradient Descent and Mini-batched Gradient Descent:

Gradient Descent is faster to compute than Stochastic Gradient Descent

Stochastic Gradient Descent is faster to compute than Mini-batched Gradient Descent

There is less noise in the gradients when using Mini-batched Gradient Descent compared to Stochastic Gradient Descent

Answer explanation

Gradient descent - gradients are calculated and a step is taken based on the whole training step. This is a computationally heavy computation, as apposed to calculating the gradients and updating the parameters based on one sample. Stochastic gradient descent results in a very noising learning signal as the gradient is sensitive to the variability of each individual datapoint. The learning signal can be smoothed out by sampling groups of datapoints in mini-batch gradient descent. Here the gradient is averaged over all datapoints.

8.

Multiple Select

1 min

1 pt

Which of the following statements about K-means are True:

The algorithm always converges

The algorithm always converges to a global optimum

The algorithm doesn’t always converge

If the algorithm does converge, it will converge to a global optimum

Answer explanation

Think about local optima. Is there a mechanism in K-means to help it get out of local minima once the algorithm has converged?

9.

Multiple Select

30 sec

1 pt

For a Gaussian Mixture Model, which of the following statements are True:

The responsibilities r_ik for ith data point sum to 1

The responsibilities r_ni for the ith mixture component sum to 1

None of the above are True

Answer explanation

The responsibilities, r_nK for a particular data point, n, represents the probability of that datapoint being generated by all the K mixture components. This is a probability distribution over all mixtures and therefore has to sum to 1. A mixture, k, however, can be responsible for generating the vast majority of data points, therefore, easily having a total responsibility over all datapoints over 1.

10.

Multiple Choice

1 min

1 pt

Which of one the following is correct?

a) sigmoid b) tanh c) ReLU

a) tanh b) sigmoid c) Linear

a) sigmoid b) tanh c) Linear

a) softmax b) tanh c) ReLU

None of the above

Answer explanation

Check definitions

11.

Multiple Select

1 min

1 pt

Which of the following functions are not suitable candidates for the activation functions of a neural network’s hidden layers?

b

c

d

e

f

Answer explanation

An in-depth knowledge of activation functions is not required here. A suitable activation function has to be 1. Differentiable (and hence excluding b) and 2. Introduce non linearity and hence not f). FYI activation function e) is know as ELU and is not examinable on this course.

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