17 Questions
What is the primary goal of problem formulation in machine learning?
To define the problem, input, output, and loss function
What technique can be used to handle unbalanced class data?
Undersample the majority and over-sample the minority
What is the purpose of exploratory data analysis (EDA) in machine learning?
To understand the distribution of the data and identify patterns
What is the purpose of the receiver operating characteristic (ROC) curve in machine learning?
To evaluate the performance of the model
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
What is the primary goal of the learning agent in model selection?
To minimize the loss function
In k-fold cross-validation, what percentage of the data is held out as a validation set in each round?
1/k
What is the purpose of the validation set in k-fold cross-validation?
To evaluate the model
Which of the following is a consideration in model selection and optimization?
Minimizing the loss function
What is the purpose of using k-fold cross-validation with different values of k?
To select the optimal value of k
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
What is a major drawback of decision trees?
They are unstable and can change significantly with the addition of a single example
What is the purpose of the validation set in model selection?
To evaluate and choose the best candidate model
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
What is the error rate of a hypothesis?
The proportion of times that h(x) ≠ y for a sample (x, y)
How can decision trees be made more widely useful?
By handling missing data, continuous and multivalued input attributes, and continuous-valued output attributes
What is the purpose of model selection?
To choose a good hypothesis space
Learn about k-fold cross-validation, a resampling technique used to evaluate machine learning models, and model selection criterion to minimize loss functions. Understand the process of splitting data into subsets and performing multiple rounds of learning. Test your knowledge of model optimization!
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