Comparison of Machine Learning Models (table)

K-nearest neighbors (KNN)

Advantages:

Simple to understand and explain
Model training is fast
Can be used for classification and regression

Disadvantages:

Must store all of the training data
Prediction phase can be slow when n is large
Sensitive to irrelevant features
Sensitive to the scale of the data
Accuracy is (generally) not competitive with the best supervised learning methods

Linear Regression

Advantages:

Simple to explain
Highly interpretable
Model training and prediction are fast
No tuning is required (excluding regularization)
Features don't need scaling
Can perform well with a small number of observations
Well-understood

Disadvantages:

Presumes a linear relationship between the features and the response
Performance is (generally) not competitive with the best supervised learning methods due to high bias
Can't automatically learn feature interactions

Logistic Regression

Advantages:

Highly interpretable (if you remember how)
Model training and prediction are fast
No tuning is required (excluding regularization)
Features don't need scaling
Can perform well with a small number of observations
Outputs well-calibrated predicted probabilities

Disadvantages:

Presumes a linear relationship between the features and the log-odds of the response
Performance is (generally) not competitive with the best supervised learning methods
Can't automatically learn feature interactions

Naive Bayes

Advantages:

Model training and prediction are very fast
Somewhat interpretable
No tuning is required
Features don't need scaling
Insensitive to irrelevant features (with enough observations)
Performs better than logistic regression when the training set is very small

Disadvantages:

Predicted probabilities are not well-calibrated
Correlated features can be problematic (due to the independence assumption)
Can't handle negative features (with Multinomial Naive Bayes)
Has a higher "asymptotic error" than logistic regression

Decision Trees

Advantages:

Can be used for regression or classification
Can be displayed graphically
Highly interpretable
Can be specified as a series of rules, and more closely approximate human decision-making than other models
Prediction is fast
Features don't need scaling
Automatically learns feature interactions
Tends to ignore irrelevant features
Non-parametric (will outperform linear models if relationship between features and response is highly non-linear)

Disadvantages:

Performance is (generally) not competitive with the best supervised learning methods
Can easily overfit the training data (tuning is required)
Small variations in the data can result in a completely different tree (high variance)
Recursive binary splitting makes "locally optimal" decisions that may not result in a globally optimal tree
Doesn't tend to work well if the classes are highly unbalanced
Doesn't tend to work well with very small datasets

Random Forests

Advantages (compared to decision trees):

Performance is competitive with the best supervised learning methods
Provides a more reliable estimate of feature importance
Allows you to estimate out-of-sample error without using train/test split or cross-validation

Disadvantages (compared to decision trees):

Less interpretable
Slower to train
Slower to predict

Regularized Linear Models

Advantages (compared to unregularized linear models):

Better performance
L1 regularization performs automatic feature selection
Useful for high-dimensional problems (p > n)

Disadvantages (compared to unregularized linear models):

Tuning is required
Feature scaling is recommended
Less interpretable (due to feature scaling)