34 Questions
What is MLOps?
A set of practices for deploying and maintaining machine learning models in production
What is CRISP DM?
A cross-industry standard process for data mining
What is the purpose of the Model Development Process by DrWhy.AI?
To provide a general idea of machine learning project workflow
Which of the following is a key focus of MLOps?
Increasing automation in production models
What is the main idea behind Decision Trees?
To learn and inference simple decision rules from the training set
Which algorithm allows the use of continuous variables as explanatory variables in Decision Trees?
C4.5
What is the full name of the CART algorithm?
Classification and Regression Trees
What is the purpose of bagging in the Random Forest algorithm?
To reduce variance of the whole model
What is feature bagging in the Random Forest algorithm?
Sampling a subset of features from the overall feature space on each level of a single tree
What is the out-of-bag error in the Random Forest algorithm?
The mean prediction error on each training sample using only the trees that did not have the sample in their bootstrap sample
Who is the author of the Random Forest algorithm?
Leo Breiman
What is a key advantage of decision trees in terms of feature selection?
Feature selection happens automatically
What is a disadvantage of decision trees in terms of overfitting?
Decision trees are prone to overfitting
What is the purpose of ensemble methods?
To improve the performance of a model by combining multiple weak models
What is bagging?
A technique for ensembling multiple models
What is a benefit of using a Random Forest model?
Random Forest can handle both continuous and categorical variables
What is the main difference between Random Forest and Extremely Randomized Trees?
Extremely Randomized Trees draws thresholds at random for each candidate feature, while Random Forest looks for the most discriminative thresholds
What is the effect of increasing the number of trees in a Random Forest model?
It increases the computation cost without improving the model beyond a critical number of trees
What is the main advantage of using Extremely Randomized Trees over Random Forest?
It reduces the variance of the model a bit more, at the expense of a slightly greater increase in bias, and is faster in terms of computational cost
What is the purpose of using decision trees in machine learning?
To serve as a starting point for more complex algorithms
What is the license under which the MLU-Explain course created by Amazon is made available?
CC BY-SA 4.0
Which machine learning model is NOT based on decision trees?
K-Nearest Neighbors
What is CART in machine learning?
A type of decision tree
What is the purpose of the YouTube tutorials on tree-building methodology mentioned in the text?
To illustrate the building process of decision trees
What is the recommended starting approach for the number of features to consider when looking for the best split in a regression problem?
100% of features
What is the recommended starting point for the number of features to consider when looking for the best split in a classification problem?
sqrt(number of features)
What is the significance of using bootstrap samples when building trees in Random Forest?
It reduces the variance and bias
What is the significance of using parallelization when estimating Random Forest?
It speeds up the learning process
What is the importance of hyperparameters in the cross-validation procedure?
They can be used to optimize the model performance
What are the key hyperparameters for the Decision Tree model?
Maximum depth of the tree, minimum number of samples required to split an internal node, minimum number of samples required to be at a leaf node, splitting criterion, number of features
What is the recommended starting value for the maximum depth of the tree hyperparameter?
3
What is the purpose of the minimum number of samples required to be at a leaf node hyperparameter?
To prevent subsequent splits if there are not enough observations to perform splitting on current tree level
What is the splitting criterion for classification?
Gini Impurity
What is the effect of setting a small value for the minimum number of samples required to split an internal node hyperparameter?
It will lead to overfitting
Take our quiz and test your knowledge on Extremely Randomized Trees! Learn about the extra layers of randomness added to the model and how it differs from Random Forest. Challenge yourself with questions on the selection process for splitting rules, and see how well you understand this advanced machine learning technique. Don't miss out on this opportunity to showcase your expertise in Extremely Randomized Trees!
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