Supervised Learning Quiz (Easy 1) with explanations

1. Question

What is the goal of Supervised Learning?

To learn a model from labeled training data that allows predictions for unseen or future data

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To cluster data into different groups based on similarity

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To find an underlying pattern in the data without using labels

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To reduce the dimensionality of the input data

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2. Question

What is the fundamental difference between Supervised Learning and Unsupervised Learning?

Supervised Learning predicts continuous values, while Unsupervised Learning predicts discrete values

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Supervised Learning uses labeled data, while Unsupervised Learning uses unlabeled data

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Supervised Learning works better on small datasets, while Unsupervised Learning requires large datasets

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Supervised Learning focuses on classification, while Unsupervised Learning focuses on regression.

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3. Question

In Supervised Learning, what do the terms ‘features’ and ‘labels’ signify?

Features are the algorithms, labels are the data

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Features are the outputs, labels are the inputs

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Features are the inputs, labels are the outputs

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Features are the data, labels are the algorithms

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4. Question

What is a ‘training set’ in Supervised Learning?

A set of algorithms

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A set of data used to test the model

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A set of labels

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A set of data used to discover potentially predictive relationships

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5. Question

What does a ‘test set’ do in Supervised Learning?

It is used to train the model

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It is used to tune the parameters of the model

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It is used to evaluate the performance of the model

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It is used to label the data

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6. Question

What does a ‘validate set’ do in Supervised Learning?

It is used to train the model

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It is used to tune the parameters of the model

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It is used to evaluate the performance of the model

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It is used to label the data

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7. Question

What is the role of a loss function in the training of a supervised model?

To regularize the model

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To determine the learning rate

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To define the architecture of the neural network

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To measure the deviation of predicted values from true values

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8. Question

What does ‘Gradient Descent’ typically refer to in Supervised Learning?

A technique to optimize the parameters of a model

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A method to categorize data

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A type of classification algorithm

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A way to split data into training and testing sets

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9. Question

In the context of supervised learning, what is the mathematical representation of the gradient in gradient descent?

The integral of the cost function over the feature space

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The Hessian matrix of the cost function

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The derivative of the cost function with respect to the model parameters

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The eigenvalues of the covariance matrix

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10. Question

What does ‘hyperparameters’ refer to in the context of Supervised Learning?

The parameters of the model that are learned from the training data

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The output values or labels of the training data

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The features or input data used to train the model

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The initial settings and configurations set by the user that guide the learning process

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11. Question

Which of the following is an example of a hyperparameter in a machine learning model?

Learning rate

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Weight coefficients

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Training data

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Features

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Supervised Learning Quiz (Easy 1)

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