Machine learning (ML) is a subfield of artificial intelligence (AI) and computer science that focuses
on using data and algorithms to mimic the way people learn, progressively improving its accuracy.
Use this tree graph as a guide to which ML algorithm to use to solve your AI problems.
(Principal
Component Analysis)
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Dimension Reduction
Combining
Variables?
PCA
Probabilistic?
SVD
(Singular Value
Descomposition)
LDA
(Linear Discriminant
Analysis)
Working with
Labeled Data?
Hierarchical?
Hierarchical
Clustering
Needs to
Specify K?
DBSCAN
(Density-based Spatial
Clustering of
Applications with Noise)
Categorical
Variables?
K-Modes
Prefer
Probability?
K-Means GMM
(Gaussian Mixture
Models)
Predicting Numbers?
Speed or
Accuracy?
Speed or
Accuracy?
Accuracy
Neural Network
Gradient Boosting Tree
Random Forest
Speed
Decision Tree
Linear Regression
Speed
Explainable?
Decision
Tree
Logistic
Regression
Accuracy
Kernel Support-
Vector Machine
Neural Network
Gradient
Boosting Tree
Random Forest
Is the Data
Too Large?
Linear Support-
Vector Machine
Naive Bayes
Naive Bayes
Feature
Selection
Feature
Extraction
Unsupervised Learning:
Dimension Reduction
Supervised Learning:
Regression
Supervised Learning:
Classification
Unsupervised Learning:
Clustering
