QUESTION: 25

Which of the following describes a neural network without an activation function?

A form of a linear regression
A form of a quantile regression
An unsupervised learning technique
A radial basis function kernel

Answer(s): A

Explanation:

A neural network without an activation function is equivalent to a form of a linear regression. A neural network is a computational model that consists of layers of interconnected nodes (neurons) that process inputs and produce outputs. An activation function is a function that determines the output of a neuron based on its input. An activation function can introduce non-linearity into a neural network, which allows it to model complex and non-linear relationships between inputs and outputs. Without an activation function, a neural network becomes a linear combination of inputs and weights, which is essentially a linear regression model.

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QUESTION: 26

The following confusion matrix is produced when a classifier is used to predict labels on a test dataset. How precise is the classifier?

48/(48+37)
37/(37+8)
37/(37+7)
(48+37)/100

Answer(s): B

Explanation:

Precision is a measure of how well a classifier can avoid false positives (incorrectly predicted positive cases). Precision is calculated by dividing the number of true positives (correctly predicted positive cases) by the number of predicted positive cases (true positives and false positives). In this confusion matrix, the true positives are 37 and the false positives are 8, so the precision is 37/(37+8) = 0.822.

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QUESTION: 27

Given a feature set with rows that contain missing continuous values, and assuming the data is normally distributed, what is the best way to fill in these missing features?

Delete entire rows that contain any missing features.
Fill in missing features with random values for that feature in the training set.
Fill in missing features with the average of observed values for that feature in the entire dataset.
Delete entire columns that contain any missing features.

Answer(s): C

Explanation:

Missing values are a common problem in data analysis and machine learning, as they can affect the quality and reliability of the data and the model. There are various methods to deal with missing values, such as deleting, imputing, or ignoring them. One of the most common methods is imputing, which means replacing the missing values with some estimated values based on some criteria. For continuous variables, one of the simplest and most widely used imputation methods is to fill in the missing values with the mean (average) of the observed values for that variable in the entire dataset. This method can preserve the overall distribution and variance of the data, as well as avoid introducing bias or noise.

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QUESTION: 28

In addition to understanding model performance, what does continuous monitoring of bias and variance help ML engineers to do?

Detect hidden attacks
Prevent hidden attacks
Recover from hidden attacks
Respond to hidden attacks

Answer(s): B

Explanation:

Hidden attacks are malicious activities that aim to compromise or manipulate an ML system without being detected or noticed. Hidden attacks can target different stages of an ML workflow, such as data collection, model training, model deployment, or model monitoring. Some examples of hidden attacks are data poisoning, backdoor attacks, model stealing, or adversarial examples. Continuous monitoring of bias and variance can help ML engineers to prevent hidden attacks, as it can help them detect any anomalies or deviations in the data or the model's performance that may indicate a potential attack.

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QUESTION: 25

Explanation:

QUESTION: 26

Explanation:

QUESTION: 27

Explanation:

QUESTION: 28

Explanation: