Machine Learning MCQ - Both supervised and unsupervised learning have input variables

Multiple choices questions in Machine learning. Interview questions on machine learning, quiz questions for data scientist answers explained, input and output variables for supervised machine learning, input variables for unsupervised machine learning algorithm, categorical variables for machine learning

Machine Learning MCQ - Supervised and unsupervised learning have at least one input variable

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1. Both supervised learning and unsupervised learning require at least one _________ .

a) output variable.

b) input variable.

c) hidden variable.

d) categorical variable.

Answer: (c) input variable In supervised learning, we have both input and output variables. But in unsupervised learning, we have only input data and no corresponding output variables. Input variables (also referred as independent variables) are features that are input to a model to predict the value of the output variables (also referred as dependent variables). In the function mentioned below, X is input variable and Y is output variable; Y = f(X) Categorical data refers to input features that represent one or more discrete items from a finite set of choices.

  

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What is input and output variables in machine learning?

Categorical variable

Which variable is common in both supervised and unsupervised machine learning algorithms?

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Machine Learning MCQ - Risk of tuning hyperparameters using test dataset

Multiple choices questions in Machine learning. Interview questions on machine learning, quiz questions for data scientist answers explained, hyperparameters, tuning hyperparameters, risk of tuning hyperparameters using test dataset, what are hyperparameters? when will the model overfit?

Machine Learning MCQ - Risk involved in tuning the hyperparameters using a test set

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1. What is the risk with tuning hyper-parameters using a test dataset?

a) Model will overfit the test set

b) Model will underfit the test set

c) Model will overfit the training set

d) Model will perform balanced

Answer: (a) Model will overfit the test set   The model will not generalize well to unseen data because it overfits the test set. Tuning model hyper-parameters to a test set means that the hyper-parameters may overfit to that test set. If the same test set is used to estimate performance, it will produce an overestimate. The test set should be used only for testing, not for parameter tuning. Using a separate validation set for tuning and test set for measuring performance provides unbiased, realistic measurement of performance.   What are hyper-parameters? Hyper-parameters are parameters whose values control the learning process and determine the values of model parameters that a learning algorithm ends up learning. We can’t calculate their values from the data. Example: Number of clusters in clustering, number of hidden layers in a neural network, and depth of a tree are some of the examples of hyper-parameters.   What is the hyper-parameter tuning? Hyper-parameter tuning is the process of choosing the right combination of hyper-parameters that maximizes the model performance. It works by running multiple trials in a single training process. Each trial is a complete execution of your training application with values for your chosen hyper-parameters, set within the limits you specify. This process once finished will give you the set of hyper-parameter values that are best suited for the model to give optimal results.

 

 

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What is hyperparameter tuning?

Which is hyperparameter?

Risk of tuning the hyperparameters using test dataset

Why does the model overfit if we tune hyperparameters using test dataset?

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