Machine Learning Algorithm Cheat Sheet - Laura Diane Hamilton

Machine Learning Algorithm Cheat Sheet - Laura Diane Hamilton: "Algorithm Pros Cons Good at
Linear regression - Very fast (runs in constant time)
- Easy to understand the model
- Less prone to overfitting - Unable to model complex relationships
-Unable to capture nonlinear relationships without first transforming the inputs - The first look at a dataset
- Numerical data with lots of features
Decision trees - Fast
- Robust to noise and missing values
- Accurate - Complex trees are hard to interpret
- Duplication within the same sub-tree is possible - Star classification
- Medical diagnosis
- Credit risk analysis
Neural networks - Extremely powerful
- Can model even very complex relationships
- No need to understand the underlying data
– Almost works by “magic” - Prone to overfitting
- Long training time
- Requires significant computing power for large datasets
- Model is essentially unreadable - Images
- Video
- “Human-intelligence” type tasks like driving or flying
- Robotics
Support Vector Machines - Can model complex, nonlinear relationships
- Robust to noise (because they maximize margins) - Need to select a good kernel function
- Model parameters are difficult to interpret
- Sometimes numerical stability problems
- Requires significant memory and processing power - Classifying proteins
- Text classification
- Image classification
- Handwriting recognition
K-Nearest Neighbors - Simple
- Powerful
- No training involved (“lazy”)
- Naturally handles multiclass classification and regression - Expensive and slow to predict new instances
- Must define a meaningful distance function
- Performs poorly on high-dimensionality datasets - Low-dimensional datasets
- Computer security: intrusion detection
- Fault detection in semiconducter manufacturing
- Video content retrieval
- Gene expression
- Protein-protein interaction"



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