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.
Dimension Reduction
Feature
Selection
Feature
Extraction
Working with
Labeled Data?
Predicting Numbers?
Combining
Variables?
Speed or
Accuracy?
PCA
Probabilistic?
SVD
(Singular Value
Descomposition)
(Principal
Component Analysis)
LDA
(Linear Discriminant
Analysis)
Accuracy
Speed
Neural Network
Gradient Boosting Tree
Random Forest
Unsupervised Learning:
Dimension Reduction
Speed or
Accuracy?
Accuracy
Kernel SupportVector Machine
Neural Network
Gradient
Boosting Tree
Random Forest
Speed
Explainable?
Hierarchical
Clustering
Needs to
Specify K?
Categorical
Variables?
Is the Data
Too Large?
Decision Tree
Linear Regression
Supervised Learning:
Regression
Hierarchical?
Naive Bayes
Supervised Learning:
Classification
Decision
Tree
Logistic
Regression
Linear SupportVector Machine
Naive Bayes
DBSCAN
(Density-based Spatial
Clustering of
Applications with Noise)
K-Modes
Unsupervised Learning:
Clustering
Prefer
Probability?
K-Means
GMM
(Gaussian Mixture
Models)
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