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WorksheetsFinTech 11-2 Classification
Total questions: 10
Worksheet time: 5mins
Which were the objectives for Classification day 2?
Feature engineering categorical features
Robust hyperparameter tuning
Decision trees and random forests
Boosting and bagging
Which are ensemble learning algorithms?
Logistic regression
Random forrests
Decision trees
Gradient boosted trees
Which functions might we use for preprocessing categorical data?
get_dummies()
to_string()
StandardScaler()
LabelEncoder()
Which model is it helpful to visualize?
Logistic regression
Random forrests
Decision trees
Support vector machine
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.
Which models use weak learners?
Random Forests
Decision Tree
XGBoost
Support Vector Machines
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)
Which are parameters for the GradientBoostClassifier model?
max_depth
max_features
sample_size
learning_rate
How was Classification day 2 for you?
Any suggestions for improvement?
