Supervised

Logistic Regression

Advantages

  • Don’t have to worry about features being correlated
  • You can easily update your model to take in new data (unlike Decision Trees or SVM)

Disadvantages

  • Deals bad with outliers
  • Must have lots of incomes for each class
  • Presence of multicollinearity

Decision Tree

Advantages

  • Easy to understand and interpret (for some people)
  • Easy to use - Doesn’t need data normalisation, dummy variables, etc
  • Can handle multi-output models
  • Easily handle feature interactions
  • Don’t have to worry about outliers

Disadvantages

  • It can be easily overfitted
  • Stability —> small changes in data can lead to completely different trees
  • If a class dominates, it can easily be biased
  • Don’t support online learning –> you should rebuilt the tree when new data comes

Ensemble Methods

Advantages

  • Harder to overfit
  • Usually better perfomance than a single model

Disadvantages

K-nearest Neighbors

Advantages

  • Little training time
  • Works well with multiclass datasets
  • Good for highly unusual data

Disadvantages

  • Need to determine value of k (distance)
  • Neighbors-based methods are known as non-generalizing machine learning methods, since they simply “remember” all of its training data
  • The accuracy of KNN can be severely degraded with high-dimension data because there is little difference between the nearest and farthest neighbor.

Gaussian Naive Bayes

Advantages

  • Need less training data tran models like logistic regression
  • Highly scalable
  • Not sensitive to irrelevant features
  • Returns the degree of certanty of the answer
  • Good when you need something fast and that perfoms well

Disavantages

  • Can’t learn interactions between features e.g., it can’t learn that although you love movies with Brad Pitt and Tom Cruise, you hate movies where they’re together).

SVM

Advantages

  • High accuracy
  • Nice theoretical guarantees regarding overfitting
  • Especially popular in text classification problems

Disavantages

  • Memory-intensive
  • Hard to interpret
  • Complicated to run and tune

Stochastic Gradient Descent

Advantages

  • Efficiency
  • Ease implementation

Disavantages

  • A lot of hyperparameters to tune
  • Sensitive to feature scaling

General References


Cheers!
Leticia

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