K-Fold Cross-Validation and Model Selection

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What is the primary goal of problem formulation in machine learning?

To collect and manage data for the problem
To define the problem, input, output, and loss function (correct)
To select the most suitable model for the problem
To evaluate the performance of the model

What technique can be used to handle unbalanced class data?

Feature engineering
Data augmentation
Model selection
Undersample the majority and over-sample the minority (correct)

What is the purpose of exploratory data analysis (EDA) in machine learning?

To select the most suitable model for the problem
To evaluate the performance of the model
To understand the distribution of the data and identify patterns (correct)
To handle missing data

What is the purpose of the receiver operating characteristic (ROC) curve in machine learning?

To evaluate the performance of the model (D) Signup and view all the answers

What is the primary goal of model monitoring and maintenance in machine learning?

To monitor the model's performance on live data and handle nonstationarity (C) Signup and view all the answers

What is the primary goal of the learning agent in model selection?

To minimize the loss function (C) Signup and view all the answers

In k-fold cross-validation, what percentage of the data is held out as a validation set in each round?

1/k (B) Signup and view all the answers

What is the purpose of the validation set in k-fold cross-validation?

To evaluate the model (B) Signup and view all the answers

Which of the following is a consideration in model selection and optimization?

Minimizing the loss function (C) Signup and view all the answers

What is the purpose of using k-fold cross-validation with different values of k?

To select the optimal value of k (D) Signup and view all the answers

What is the relationship between the complexity of the model and the error rate on the training data?

The error rate decreases as the complexity of the model increases (B) Signup and view all the answers

What is a major drawback of decision trees?

They are unstable and can change significantly with the addition of a single example (C) Signup and view all the answers

What is the purpose of the validation set in model selection?

To evaluate and choose the best candidate model (A) Signup and view all the answers

What is the learning curve for a decision tree learning algorithm?

A graph showing the relationship between the number of training examples and the average error rate (A) Signup and view all the answers

What is the error rate of a hypothesis?

The proportion of times that h(x) ≠ y for a sample (x, y) (C) Signup and view all the answers

How can decision trees be made more widely useful?

By handling missing data, continuous and multivalued input attributes, and continuous-valued output attributes (D) Signup and view all the answers

What is the purpose of model selection?

To choose a good hypothesis space (D) Signup and view all the answers