FinTech 11-2 Classification
Quiz
•
Computers, Other
•
Professional Development
•
Hard
Louis Burns
Used 20+ times
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10 questions
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1.
MULTIPLE SELECT QUESTION
30 sec • 1 pt
Which were the objectives for Classification day 2?
Feature engineering categorical features
Robust hyperparameter tuning
Decision trees and random forests
Boosting and bagging
2.
MULTIPLE SELECT QUESTION
30 sec • 1 pt
Which are ensemble learning algorithms?
Logistic regression
Random forrests
Decision trees
Gradient boosted trees
3.
MULTIPLE SELECT QUESTION
30 sec • 1 pt
Which functions might we use for preprocessing categorical data?
get_dummies()
to_string()
StandardScaler()
LabelEncoder()
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which model is it helpful to visualize?
Logistic regression
Random forrests
Decision trees
Support vector machine
5.
MULTIPLE SELECT QUESTION
30 sec • 1 pt
What can you learn from the decision tree visualization?
How many nodes and branches the model has.
How deep the tree goes.
How accurate the model is.
Whether the model might be overfitting.
6.
MULTIPLE SELECT QUESTION
30 sec • 1 pt
Which models use weak learners?
Random Forests
Decision Tree
XGBoost
Support Vector Machines
7.
MULTIPLE SELECT QUESTION
30 sec • 1 pt
What are ways to improve the performance of random forests?
Increase the number of estimators
Additional feature engineering
Change the random state
Use gradient boosting (XGBoost)
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