What is Explainable Artificial Intelligence (XAI)?
1. A type of artificial intelligence that is transparent and interpretable to humans.
2. A type of artificial intelligence that is designed to mimic human intelligence.
3. A type of artificial intelligence that can learn and adapt from experience.
4. A type of artificial intelligence that is optimized for a specific task or objective.
Explanation: Explainable Artificial Intelligence (XAI) is a type of artificial intelligence that is transparent and interpretable to humans. It aims to make machine learning models and decision-making processes more transparent and understandable to users and stakeholders. XAI techniques include feature importance analysis, saliency maps, decision trees, and other methods for visualizing and explaining the internal workings of AI models. XAI is an important research area in AI ethics and regulation.
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What is the difference between precision and recall?
1. Precision measures how many of the predicted positive cases are actually positive, while recall measures how many of the actual positive cases were correctly identified by the model
2. Precision measures how many of the actual positive cases were correctly identified by the model, while recall measures how many of the predicted positive cases are actually positive
3. Precision and recall are the same thing
4. Precision and recall are both measures of how well a model is able to identify positive cases
Explanation: Precision and recall are both measures of how well a model is able to identify positive cases, but they focus on different aspects of this task. Precision measures how many of the predicted positive cases are actually positive, while recall measures how many of the actual positive cases were correctly identified by the model.
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What is the difference between a random forest and a gradient boosting machine?
1. Random forest is an ensemble of decision trees while gradient boosting is a single decision tree
2. Random forest combines decision trees using boosting while gradient boosting combines decision trees using bagging
3. Random forest uses bagging while gradient boosting uses boosting
4. Random forest is used for regression while gradient boosting is used for classification
Explanation: Random forest is an ensemble of decision trees that combines the results of multiple decision trees using bagging. Gradient boosting is also an ensemble of decision trees, but it combines the results of multiple decision trees using boosting.
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❓ Question 12:
What is an autoencoder?
1. A type of decision tree algorithm
2. A neural network architecture that is used for dimensionality reduction
3. A method for unsupervised learning
4. A type of clustering algorithm
✅ Correct Response:2
Explanation:An autoencoder is a neural network architecture that is used for dimensionality reduction, and is particularly useful for reducing the dimensionality of high-dimensional datasets.
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What is an autoencoder?
1. A type of decision tree algorithm
2. A neural network architecture that is used for dimensionality reduction
3. A method for unsupervised learning
4. A type of clustering algorithm
✅ Correct Response:
Explanation:
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What is the purpose of the iter() function when used with an iterable?
Option 1: It returns an iterator object that can be iterated over
Option 2: It checks if an object is iterable
Option 3: It converts an iterable into a generator
Option 4: It raises a StopIteration exception
Explanation:
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The find() method returns -1 if a substring is not found in a string.
Option 1: TRUE
Option 2: FALSE
Option 3: nan
Option 4: nan
Explanation:
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What is the result of the expression "10 % 3" in Python?
Option 1: 1
Option 2: 3
Option 3: 0
Option 4: 3.333
Explanation:
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The expression "x in [1, 2, 3]" is True when:
Option 1: x is equal to 1 or 2 or 3
Option 2: x is equal to any value in the list
Option 3: x is not equal to any value in the list
Option 4: x is equal to the length of the list
Explanation:
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The ____ method in Pandas is used to replace specific values in a DataFrame.
Option 1: replace()
Option 2: exchange()
Option 3: transform()
Option 4: change()
Explanation:
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To drop rows with a specific value in a DataFrame column, we can use the ____ method in Pandas.
Option 1: remove()
Option 2: delete()
Option 3: drop()
Option 4: dismiss()
Explanation:
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Which of the following methods are commonly used for derivative-based optimization in SciPy?
Option 1: fmin_bfgs
Option 2: minimize with method='BFGS'
Option 3: minimize with method='Newton-CG'
Option 4: fmin
Explanation:
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The length of an array in C++ is significant because it determines ____.
1. the number of elements that can be stored in the array
2. the memory allocated for the array
3. the maximum size of the array
4. All of the above
Explanation:
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To access elements in a structure in C++, you use the structure variable name followed by ___.
1. A dot operator (.)
2. An arrow operator (->)
3. A hash sign (#)
4. A dollar sign ($)
Explanation:
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❔ Question 22: #python
What does the 'map' function in Python do?
Option 1: Applies a function to each element of an iterable and returns a map object.
Option 2: Filters elements from an iterable based on a given function.
Option 3: Combines multiple iterables into a single iterable.
Option 4: Sorts the elements of an iterable in ascending order.
✅ Correct Response:1
Explanation:The 'map' function in Python applies a function to each element of an iterable and returns a map object containing the results. This is commonly used to apply the same operation to every item in a list or other iterable data structure.
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What does the 'map' function in Python do?
Option 1: Applies a function to each element of an iterable and returns a map object.
Option 2: Filters elements from an iterable based on a given function.
Option 3: Combines multiple iterables into a single iterable.
Option 4: Sorts the elements of an iterable in ascending order.
✅ Correct Response:
Explanation:
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❔ Question 22: #python What does the 'map' function in Python do? Option 1: Applies a function to each element of an iterable and returns a map object. Option 2: Filters elements from an iterable based on a given function. Option 3: Combines multiple iterables…
If you answered correctly, show this reaction 👍
and otherwise 👎
and otherwise 👎
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❔ Question 23: #python
What does the 'sorted' function in Python do?
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What does the 'sorted' function in Python do?
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Anonymous Quiz
7%
Returns a reversed copy of the given iterable.
86%
Returns a new sorted list from the elements of the given iterable.
4%
Converts a string into a list of characters.
2%
Returns the maximum element from the given iterable.
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❔ Question 25: #python
What does the 'max' function in Python do?
What does the 'max' function in Python do?
Anonymous Quiz
4%
Returns the minimum element from the given iterable.
3%
Returns the average of all elements in an iterable.
4%
Converts a list of strings into a single string.
89%
Returns the maximum element from the given iterable.
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