> ML_ALGORITHM // NAIVE-BAYES_v1.0
Naive Bayes Classifiers (Gaussian, Multinomial, Bernoulli)
Fast probabilistic classifier applying Bayes theorem under the naive assumption of conditional feature independence given the class.
Bayesian & Probabilistic Modelsclassical-supervisedhigh-intrinsicsmall (<1k)
Back to All AlgorithmsComputational Complexity
Training Complexity:O(n * p) single pass counting
Inference Complexity:O(p)
Hardware Profile
CPU Friendly:Yes
Requires GPU:No
Memory Footprint:low
Interpretability & Data
Interpretability Tier:high-intrinsic
Training Data Needs:small (<1k)
Interpretability Assessment
Individual log-likelihood contributions per word or feature can be summed and inspected.
Suitable Tasks & Supported Modalities
Suitable Tasks:
text classificationbinary classificationmulticlass classification
Supported Modalities:
tabulartext
Implementing Libraries
Foundational Literature
An Essay towards solving a Problem in the Doctrine of ChancesThomas Bayes, Richard Price (1763) · Philosophical Transactions of the Royal Society of London
Common Pitfalls & Warnings
- Zero-probability problem on unobserved words without Laplace/Lidstone smoothing
- Strong correlated features multiplying confidence artificially