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Old 13 Jan 2018, 02:46 AM   #682715 / #1
lpetrich
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Default Machine-Learning Algorithms

Which machine learning algorithm should I use? - Subconscious Musings It depends on what one wants to do. Look for patterns? (unsupervised) Predict some outputs from some inputs? (supervised)

Dimensional reduction can be a good first step, because if much of the variation is in a few directions, one can project the data onto those directions and get a less bulky dataset for other methods.

Data can be either numeric or categorical, being a member of some category. For example, a pet can be a dog, a cat, a rodent, a bird, a reptile, a fish, a tarantula, ...


I'll write out that article's flowchart as pseudocode.

Dimension Reduction?
Yes:
. Unsupervised Learning: Dimension Reduction
No:
. (do nothing)
End

Have Responses?
Yes:
. Predicting Numeric?
. Yes:
. . Supervised Learning: Regression
. No:
. . Supervised Learning: Classification
. End
No:
. Unsupervised Learning: Clustering
End

Here are the subcategories.

Unsupervised Learning: Dimension Reduction:
Topic Modeling?
Yes:
. Probabilistic?
. Yes:
. . Latent Dirichlet Analysis
. No:
. . Singular Value Decomposition
. End
No:
. Principal Component Analysis
End

Unsupervised Learning: Clustering:
Hierarchical?
Yes:
. Hierarchical
No:
. Need to Specify k?
. Yes:
. . Categorical Variables?
. . Yes:
. . . k-modes
. . No:
. . . Prefer Probability?
. . . Yes:
. . . . Gaussian Mixture Model
. . . No:
. . . . k-means
. . End
. No:
. . DBScan
. End
End

Supervised Learning: Regression:
Speed or Accuracy?
Speed:
. Decision Tree, Linear Regression
Accuracy:
. Random Forest, Neural Network, Gradient Boosting Tree
End

Supervised Learning: Classification:
Speed or Accuracy?
Speed:
. Explainable?
. Yes:
. . Decision Tree, Logistic Regression
. No:
. . Data Is Too Large?
. . Yes:
. . . Naive Bayes
. . No:
. . . Linear SVM, Naive Bayes
. End
Accuracy:
. Kernel SVM, Random Forest, Neural Network, Gradient Boosting Tree
End


As one can see, this is a well-developed field, though one being extended with "deep learning" algorithms for huge datasets like big collections of image files.
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