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PyBrain MCQs

PyBrain MCQs: This section contains multiple-choice questions on PyBrain. All MCQs have the correct answers and explanations. These MCQs will help students and professionals to test their skills and to enhance their knowledge of PyBrain.

List of PyBrain MCQs

1. Pybrain is an open-source machine learning package written in ____.

  1. C
  2. Ruby on Rails
  3. Java
  4. Python

Answer: D) Python

Explanation:

Pybrain is an open-source machine learning package written in Python.

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2. PyBrain is an abbreviation for ____.

  1. Python-Based Reinforcement Learning
  2. AI
  3. Neural Network Library
  4. All of the above

Answer: D) All of the above

Explanation:

PyBrain is an abbreviation for Python-Based Reinforcement Learning, AI, and Neural Network Library.

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3. A network is made up of modules that are linked together through ____.

  1. Contacts
  2. Modules
  3. Connections
  4. Connectors

Answer: C) Connections

Explanation:

A network is made up of modules that are linked together through connections.

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4. Does PyBrain support neural networks?

  1. Yes
  2. No

Answer: A) Yes

Explanation:

Yes, Pybrain supports neural networks such as the Feed-Forward Network, the Recurrent Network, and others.

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5. What is the feed-forward network?

  1. A feed-forward network is a type of neural network in which information between nodes flows backward and never forward.
  2. A feed-forward network is a type of neural network in which information between nodes flows forward and never backward.

Answer: B) A feed-forward network is a type of neural network in which information between nodes flows forward and never backward.

Explanation:

A feed-forward network is a type of neural network in which information between nodes flows forward and never backward.

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6. Which is the first and most basic network accessible in the artificial neural network?

  1. Perceptron.
  2. Feed Forward Neural Network.
  3. Multilayer Perceptron.
  4. Convolutional Neural Network

Answer: A) Perceptron.

Explanation:

Perceptron is the first and most basic network accessible in the artificial neural network.

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7. What is the difference between Recurrent Networks and Feed Forward Networks?

  1. Recurrent Networks are similar to Feed Forward Networks it is not compulsory in Recurrent Networks to remember the data at each step.
  2. Recurrent Networks are similar to Feed Forward Networks in that they must remember the data at each step. Each step's history must be kept.
  3. There is no difference they are the same.

Answer: B) Recurrent Networks are similar to Feed Forward Networks in that they must remember the data at each step. Each step's history must be kept.

Explanation:

Recurrent Networks are similar to Feed Forward Networks in that they must remember the data at each step. Each step's history must be kept.

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8. Which of the following datasets are supported by PyBrain?

  1. Supervised dataset
  2. Classification dataset
  3. Sequential dataset
  4. All of the above

Answer: D) All of the above

Explanation:

Supervised dataset, classification dataset, and sequential dataset are the datasets classes that are supported by Pybrain.

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9. Which type of dataset has a temporal link between the input and output pairs?

  1. Supervised dataset
  2. Classification dataset
  3. Sequential dataset

Answer: C) Sequential dataset

Explanation:

Sequential dataset is a dataset that has a temporal link between the input and output pairs.

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10. In which type of dataset each sample is assigned a label from a limited range of categories?

  1. Supervised dataset
  2. Classification dataset
  3. Sequential dataset

Answer: B) Classification dataset

Explanation:

A classification dataset is a type of dataset in which each sample is assigned a label from a limited range of categories.

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11. What is BackpropTrainer?

  1. BackpropTrainer is a trainer that backpropagates the dataset in a forward direction to train the parameters of a module using a supervised or ClassificationDataSet dataset.
  2. BackpropTrainer is a trainer that backpropagates mistakes to train the parameters of a module using a supervised or ClassificationDataSet dataset

Answer: B) BackpropTrainer is a trainer that backpropagates mistakes to train the parameters of a module using a supervised or ClassificationDataSet dataset

Explanation:

BackpropTrainer is a trainer that backpropagates mistakes to train the parameters of a module using a supervised or ClassificationDataSet dataset.

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12. Which of the following is used to train the module until it converges on the dataset?

  1. TrainUntilConvergence
  2. BackpropTrainer
  3. Both

Answer: A) TrainUntilConvergence

Explanation:

TrainUntilConvergence is used to train the module until it converges on the dataset.

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13. Does Pybrain integrates with other Python libraries/packages?

  1. Yes
  2. No

Answer: A) Yes

Explanation:

Pybrain integrates seamlessly with other Python packages for data visualization.

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14. Can you work with.csv files to import datasets in Pybrain?

  1. Yes
  2. No

Answer: A) Yes

Explanation:

Working with.csv files to import datasets is a breeze with Pybrain. It also permits the use of datasets from another library.

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15. What do you mean by Total Error?

  1. Total Error refers to the error shown after the dataset is loaded.
  2. Total Error refers to the error shown after the wrong format of the dataset is loaded.
  3. Total Error refers to the error shown after the network is trained.

Answer: C) Total Error refers to the error shown after the network is trained.

Explanation:

Total Error refers to the error shown after the network is trained.

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16. What is trained data?

  1. Trained data is the data that was used to train the Pybrain network.
  2. Trained data is the data that was loaded in the Pybrain.
  3. Trained data is the data that is the result of the dataset loaded in the Pybrain network.

Answer: A) Trained data is the data that was used to train the Pybrain network.

Explanation:

Trained data is the data that was used to train the Pybrain network.

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17. The data which is used to test the trained Pybrain network is known as ____.

  1. Testing info
  2. Testing outcome
  3. Testing evidence
  4. Testing data

Answer: D) Testing data

Explanation:

The data which is used to test the trained Pybrain network is known as testing data.

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18. ____ are essentially a collection of functions that are employed on a network's hidden layers.

  1. Layers
  2. Tiers
  3. Path
  4. Strata

Answer: A) Layers

Explanation:

Layers are essentially a collection of functions that are employed on a network's hidden layers.

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19. Does connection work exactly similarly to a layer?

  1. Yes
  2. No

Answer: B) No

Explanation:

A connection works similarly to a layer; the only difference is that it transfers data from one node in a network to another.

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20. ____ are networks that have input and output buffers.

  1. Units
  2. Components
  3. Modules

Answer: C) Modules

Explanation:

Modules are networks that have input and output buffers.

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21. Which of the following package should be added so that we can utilize the dataset?

  1. pybrain.data
  2. pybrain.dataset
  3. pybrain.datasetSuper
  4. pybrain.Superdataset

Answer: B) pybrain.dataset

Explanation:

We can utilize the dataset by adding the following package: pybrain.dataset

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22. Which of the following Supervised dataset method will create a new sample from the input and target?

  1. addValue(inp, target)
  2. add(inp, target)
  3. addTest(inp, target)
  4. addSample(inp, target)

Answer: D) addSample(inp, target)

Explanation:

addSample(inp, target) method will create a new sample from the input and target.

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23. The ____ function returns a deep copy of the dataset.

  1. Clone()
  2. Dupli()
  3. Copy()
  4. Replica()

Answer: C) Copy()

Explanation:

The copy() function returns a deep copy of the dataset.

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24. ____ function clears the dataset.

  1. Clean()
  2. Remove()
  3. Delete()
  4. Eliminate()

Answer: A) Clean()

Explanation:

clean() function clears the dataset.

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25. Which of the following is needed to be imported in Pybrain to create a feedforward network?

  1. pybrain.form import FeedForwardNetwork
  2. pybrain import FeedForwardNetwork
  3. pybrain.structure import FeedForwardNetwork
  4. pybrain.system import FeedForwardNetwork

Answer: C) pybrain.structure import FeedForwardNetwork

Explanation:

To create a feedforward network, we need to import it from pybrain structure as:- from pybrain.structure import FeedForwardNetwork

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26. To create a layer, you need to use ____ class as the base class to create all types of layers.

  1. NeuroLayer class
  2. NeuronLayer class
  3. NeuronalLayer class

Answer: B) NeuronLayer class

Explanation:

To create a layer, you need to use NeuronLayer class as the base class to create all types of layers.

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27. To create a Layer, we need to implement which method?

  1. _forwardImplementation()
  2. _backwardImplementation().
  3. Both

Answer: C) Both

Explanation:

To create a Layer, we need to implement two methods: _forwardImplementation() and _backwardImplementation().

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28. How many parameters does the _forwardImplementation() function accepts?

  1. 5
  2. 4
  3. 3
  4. 2

Answer: D) 2

Explanation:

The _forwardImplementation() function accepts two parameters, inbuf, and outbuf.

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29. Which function computes the derivative of the output with regard to the input?

  1. _forwardImplementation()
  2. _backwardImplementation().
  3. Both

Answer: B) _backwardImplementation().

Explanation:

The _backwardImplementation() function computes the derivative of the output with regard to the input.

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30. Which of the following component interact with each other during reinforcement?

  1. Environment
  2. Agent
  3. Task
  4. Experiment
  5. All of the above

Answer: E) All of the above

Explanation:

The components that interact with each other during Reinforcement are as follows –

  • Environment
  • Agent
  • Task
  • Experiment

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31. To link the agent to the environment, we need a unique component called ____.

  1. Job
  2. Experiment
  3. Task

Answer: C) Task

Explanation:

To link the agent to the environment, we need a unique component called to task.

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32. What is MDP?

  1. Maze decision process
  2. Markov decision process
  3. Markup decision process

Answer: B) Markov decision process

Explanation:

MDP stands for Markov decision process.

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33. Which API Adds a connection to the network?

  1. addConnection(c)
  2. includeConnection(c)
  3. appendConnection(c)
  4. CreateConnection(c)

Answer: A) addConnection(c)

Explanation:

addConnection(c) API, Adds a connection to the network.

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34. Which of the following Adds the given module to the network?

  1. IncludeModule(m)
  2. addedModule(m)
  3. aprehendModule(m)
  4. addModule(m)

Answer: D) addModule(m)

Explanation:

addModule(m) API, Adds the given module to the network.

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35. ____ Adds the module given to the network and marks it as an input module.

  1. addInputMod(m)
  2. addInputModule(m)
  3. addInput(m)
  4. addModule(m)

Answer: B) addInputModule(m)

Explanation:

addInputModule(m) − Adds the module given to the network and mark it as an input module.

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36. ____ Adds the module to the network and mark it as an output module.

  1. addOutModule(m)
  2. addOutputMod(m)
  3. addOutputModule(m)
  4. addOutput(m)

Answer: C) addOutputModule(m)

Explanation:

addOutputModule(m) − Adds the module to the network and marks it as an output module.

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37. Which of the following function is used to load a dataset in PyBrain?

  1. Load()
  2. Dataset()
  3. loadDataSet()

Answer: C) loadDataSet()

Explanation:

To load a dataset in PyBrain, you can use the loadDataSet() function.

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38. In PyBrain, a ____ is a computational component that processes inputs, generates outputs and executes computations.

  1. Neuron
  2. Neuro
  3. Neuro Unit
  4. Neuro operation

Answer: A) Neuron

Explanation:

In PyBrain, a neuron is a computational component that processes inputs, generates outputs and executes computations.

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39. In PyBrain, which of the following term refers to the process of computing a neuron's output depending on its inputs and weights?

  1. Electrification
  2. Activation
  3. Neurofunction
  4. Neurotisation

Answer: B) Activation

Explanation:

In PyBrain, the term "activation" refers to the process of computing a neuron's output depending on its inputs and weights.

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40. What is a Bias term?

  1. In PyBrain, a bias term is a constant value that is added to a neuron's inputs before activation.
  2. In PyBrain, a bias term is a variable value that is added to a neuron's output before activation.

Answer: A) In PyBrain, a bias term is a constant value that is added to a neuron's inputs before activation.

Explanation:

In PyBrain, a bias term is a constant value that is added to a neuron's inputs before activation. It can contribute to a network's performance improvement by adding more flexibility.

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41. To set the bias term for a neuron in PyBrain, which method is utilized?

  1. setBias()
  2. set()
  3. Bias()

Answer: A) setBias()

Explanation:

To set the bias term for a neuron in PyBrain, you can use the setBias() method of the Neuron object.

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42. In PyBrain, ____ is the act of introducing a penalty term into the error function to prevent overfitting.

  1. Cross validation
  2. Normalization
  3. Regularisation

Answer: C) Regularisation

Explanation:

In PyBrain, regularisation is the act of introducing a penalty term into the error function to prevent overfitting.

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43. In PyBrain, ____ technique is used to assess how well a neural network performs by breaking the dataset into a number of subgroups and training the network on each subset separately.

  1. Cross-validation
  2. Normalization
  3. Regularisation
  4. Cross verificaion

Answer: A) Cross-validation

Explanation:

In PyBrain, a technique called cross-validation is used to assess how well a neural network performs by breaking the dataset into a number of subgroups and training the network on each subset separately.

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44. Which algorithm Pybrain does not support?

  1. Backpropagation
  2. Radial Basis Function Networks
  3. Genetic Algorithms
  4. K-Nearest Neighbours

Answer: D) K-Nearest Neighbours

Explanation:

K- Nearest Neighbours algorithm Pybrain does not support.

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45. Which of the following trainers does Pybrain supports?

  1. Backpropagation trainers
  2. Reinforcement trainers
  3. Unsupervised trainers.
  4. All of the above

Answer: D) All of the above

Explanation:

PyBrain supports a variety of trainers, including:

  • Backpropagation trainers
  • Reinforcement trainers, and
  • Unsupervised trainers.

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