Low computational cost

Uses missing data to inform model

Can handle large number of records and variables

Provides estimates of the information gained by each input variable

Works well with nonlinear data

Not ideal for rare outcomes

Very difficult to interpret individual variable contributions to classification

Time consuming hyperparameter tuning

Overfitting of data

Low computationally cost

Effective when number of variables> number of records (very wide data)

Need a clear margin of separation between outcomes (unhealthy drinking vs low-risk)

Time consuming hyperparameter tuning

Not efficient with large number of records

Works well with nonlinear data

High computational cost during training

Extremely useful with large number of predictors (high dimensionality (e.g. image data))

Time consuming hyperparameter tuning

Any numeric data can be used

Need relatively large number of records for training set

Very difficult to interpret individual variable contributions to classification

Must have many records per variable

Overfitting of data

Very simple construction requiring minimal specifications (a.k.a. hyperparameters)

Intuitive methodology

High computational cost

Challenging with large number of variables (wide data)

Cannot handle imbalanced data

Very sensitive to outliers

Cannot handle missing data

Can handle missing data

Highly biased to training set

No data preprocessing needed

Provides highly intuitive explanation over the prediction

Relatively inaccurate compared to other models

Common and understood by most

Proper selection of features is required

Relatively easy to implement

Cannot handle missing data

Loss function is always convex

Needs data preprocessing and handling to cover non-linear data

Cannot handle large number of categorical predictors