Practical ML Quiz 1: Forests and Features
Practical ML Quiz: Forests and Features
Test your knowledge on machine learning concepts, focusing on forests and features with our comprehensive quiz. Whether you're just beginning or looking to refine your expertise, this quiz covers essential topics related to Random Forests, GBDT, and model evaluation.
- 11 engaging questions to challenge your understanding
- Multiple choice and checkbox formats for diverse learning
- Designed for machine learning enthusiasts and practitioners
We've trained a RandomForest model with N trees. Consider two cases:
1. Drop the first tree
2. Drop the last tree
Then compare models performance on the train set. Select the right answer:
In the case1 performance will drop more than in the case2
In the case1 performance will be roughly the same as in the case2
In the case1 performance will drop less than in the case2
We've trained a GBDT (gradient boost decision tree) with N trees and a fairly high learning rate. Consider two cases:
1. Drop the first tree
2. Drop the last tree
We then compare models performance on the train set. Select the right answer.
In the case1 performance will drop more than in the case2
In the case1 performance will be roughly the same as in the case2
In the case1 performance will drop less than in the case2
Consider the two cases:
1. Fit two RandomForestClassifiers 500 trees each and average their predicted probabilities on the test set.
2. Fit one RandomForestClassifier with 1000 trees and use it to get test set probabilities.
All other hyperparameters are the same for all models. Select the right answer.
The quality of predictions in the case1 will be roughly the same
The quality of predictions in the case1 will be higher
The quality of predictions in the case1 will be lower
From left to right, what model was most probably used to produce such decision surface?
Decision Tree, Random Forest, K Nearest Neighbours, Linear Regression
Decision Tree, K Nearest Neighbours, Random Forest, Linear Regression
K Nearest Neighbours, Decision Tree, Random Forest, Linear Regression
Linear Regression, K Nearest Neighbours, Random Forest, Decision Tree
Random Forest, Decision Tree, K Nearest Neighbours, Linear Regression
K Nearest Neighbours, Linear Regression, Decision Tree, Random Forest
Select the correct statement about the RandomForest and GBDT models.
Trees in RandomForest can be constructed in parallel
In GBDT trees are independent from each other
Trees in GBDT can be constructed in parallel
In RandomForest each new tree is built to improve the previous trees
Suppose we have a feature with all the values between 0 and 1 except few outliers larger than 1
What can help to decrease outliers' influence on non-tree models? Select ALL that applies
Apply rank transform to the features
Apply np.log(x+1) transform to the data
Apply np.sqrt(x) transform to the data
Winsorization
StandardScaler
MinMaxScaler
Suppose we fit a tree-based model. In which cases label encoding can be better to use than one-hot encoding?
Select ALL that applies
When categorical feature is ordinal
When the number of categorical features in the dataset is huge
When categorical features have high correlation with the ground truth
Suppose we have a categorical feature and a linear model. We need to somehow encode this feature. Which of the following statements are true?
Depending on the dataset either of label encoder or one-hot encoder could be better
Label encoding is always better than one-hot encoding
One-hot encoding is always better than label encoding
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