FinTech 11-2 Classification

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?