Machine learning quiz

Do you know your machine learning? Test your M.L. skills with this quiz!

1 / 16

What is the main advantage of using a support vector machine?

Ability to handle non-linear relationships

Relatively low computational cost

Ability to handle large data sets

Ability to handle a high number of features

2 / 16

What is the difference between a decision tree and a random forest?

Decision trees are used for classification tasks, while random forests are used for regression tasks

Decision trees are composed of fewer trees, while random forests are composed of more trees

Decision trees are more accurate, while random forests are less accurate

Decision trees are more computationally expensive, while random forests are less computationally expensive

3 / 16

What is the difference between supervised and semi-supervised learning?

Supervised learning requires a test set, while semi-supervised learning does not

Supervised learning requires labeled data, while semi-supervised learning does not

Supervised learning is used for classification tasks, while semi-supervised learning is used for regression tasks

Supervised learning relies on artificial intelligence, while semi-supervised learning does not

4 / 16

What is the purpose of feature engineering?

To select the most suitable machine learning algorithm

To improve the accuracy of a machine learning model

To extract relevant features from a dataset

To fine-tune the parameters of a machine learning model

5 / 16

What is the difference between a shallow and a deep neural network?

Shallow neural networks are composed of fewer layers, while deep neural networks are composed of more layers

Shallow neural networks are more accurate, while deep neural networks are less accurate

Shallow neural networks are more computationally expensive, while deep neural networks are less computationally expensive

Shallow neural networks are used for classification tasks, while deep neural networks are used for regression tasks

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What is the difference between a Bayesian and a frequentist approach?

Bayesian approaches are used for supervised learning, while frequentist approaches are used for unsupervised learning

Bayesian approaches use probability to make predictions, while frequentist approaches use statistics

Bayesian approaches are used for classification tasks, while frequentist approaches are used for regression tasks

Bayesian approaches are more computationally expensive, while frequentist approaches are less computationally expensive

7 / 16

What is a latent variable?

A variable that is not directly observed, but can be inferred from other variables

A variable that is directly observed and can be used to predict other variables

A variable that is used to measure a certain construct

A variable that is used to group data points into clusters

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What is the goal of clustering?

To detect anomalies

To identify patterns

To predict outcomes

To classify data

9 / 16

What is regularization?

A technique for selecting the most suitable machine learning algorithm

A technique for improving the accuracy of a machine learning model

A technique for fine-tuning the parameters of a machine learning model

A technique for extracting features from a dataset

10 / 16

What is the difference between a generative and a discriminative model?

Generative models are used for classification tasks, while discriminative models are used for regression tasks

Generative models are used for regression tasks, while discriminative models are used for classification tasks

Generative models are used to approximate a distribution, while discriminative models are used to approximate a function

Generative models are used to approximate a function, while discriminative models are used to approximate a distribution

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What is a convolutional neural network?

A type of deep learning algorithm

A type of reinforcement learning algorithm

A type of supervised learning algorithm

A type of unsupervised learning algorithm

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What is the main advantage of using a gradient boosting algorithm?

Ability to handle non-linear data

Relatively low computational cost

Ability to handle large data sets

Ability to handle a high number of features

13 / 16

What is the goal of reinforcement learning?

To minimize the number of errors

To maximize the reward

To minimize the cost

To maximize the accuracy

14 / 16

What is the main purpose of a neural network?

To classify data

To extract features from data

To approximate complex functions

To optimize model parameters

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What is the difference between supervised and unsupervised learning?

Supervised learning requires labeled data, while unsupervised learning does not

Supervised learning relies on artificial intelligence, while unsupervised learning does not

Supervised learning is used for classification tasks, while unsupervised learning is used for clustering tasks

Supervised learning requires a test set, while unsupervised learning does not

16 / 16

What is hyperparameter tuning?

A process for tweaking the modelk performance for optimal results

A process for fine-tuning the parameters of a machine learning model

A process for selecting the most suitable machine learning algorithm

A process for extracting features from a dataset

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