API Reference - Models

Remember! This is source-available library and not open-source one! You may have a look for more details here.
If you wonder what are the most high-value use cases that helps with revenue and retention generation this DataPredict™, you can view them here!
To see which algorithms that you can swap model parameters with other types of algorithms, you can view them here!

Model Type	Purpose	Count
Regression	Continuous Value Prediction	13
Classification	Feature-Class Prediction	13
Clustering	Feature Grouping	10
Deep Reinforcement Learning	State-Action Optimization Using Neural Networks	26
Tabular Reinforcement Learning	State-Action Optimization Using Tables	17
Sequence Modelling	Next State Prediction And Generation	3
Filtering	Next State Tracking / Estimation	4
Outlier Detection	Outlier Score Generation	4
Recommendation	User-Item Pairing	5
Generative	Feature To Novel Value	4
Feature-Class Containers	Feature-Class Look Up	1
Total		100

Legend

Icon	Name	Description
❗	Implementation Issue	The model may have some implementation problems.
🔰	Beginner Algorithm	Commonly taught to beginners.
💾	Data Efficient	Require few data to train the model.
⚡	Computationally Efficient	Require few computational resources to train the model.
🛡️	Noise Resistant	Can handle randomness / unclean data.
🟢	Online	Can adapt real-time.
🟡	Session-Adaptive / Offline	Can be retrained each session.
⚠️	Assumption-Heavy	Have restrictive rules on using the model.
⚙️	Configuration-Heavy	Requires a lot of manual configuration to use.

Note

For strong deep learning applications, have a look at DataPredict™ Neural (object-oriented, static graph) and DataPredict™ Axon (function-oriented, dynamic graph) instead. DataPredict™ is only suitable for general purpose machine, deep and reinforcement learning.
- Uses reverse-mode automatic differentiation and lazy differentiation evaluation.
- Includes convolutional, pooling, embedding, dropout and activation layers.
- Contains most of the deep reinforcement learning and generative algorithms listed here.
Currently, DataPredict™ has 93% (93 out of 100) models with online learning capabilities. By default, most models would perform offline / batch training on the first train before switching to online / incremental / sequential after the first train.
No dimensionality reduction algorithms due to not being suitable for game-related use cases. They tend to be computationally expensive and are only useful when a full dataset is collected. This can be offset by choosing proper features and remove the unnecessary ones.
No tree models (like decision trees) for now due to these models requiring the full dataset and tend to be computationally expensive. In addition, most of these tree models do not have online learning capabilities.

Regression

Model	Alternate Names	Properties	Use Cases
LinearRegression	LR	🔰 🟢 🟡	General Time-To-Leave Prediction And In-Game Currency Price Generation
QuantileRegression	None	🟢 🟡	Case-Based Time-To-Leave Prediction And In-Game Currency Price Generation
PoissonRegression	None	🟢 🟡 ⚠️	Positive-Integer-Based Time-To-Leave Prediction And In-Game Currency Price Generation
NegativeBinomialRegression	None	🟢 🟡 ⚠️	Positive-Integer-Based Time-To-Leave Prediction And In-Game Currency Price Generation
GammaRegression	None	❗ 🟢 🟡 ⚠️	Player Session Duration Prediction And Content Engagement Time Prediction
IsotonicRegression	None	⚡ 🟢 🟡	1-Dimensional Skill-Based Time-To-Leave Prediction
PassiveAggressiveRegressor	PA-R	⚡ 🟢	Fast Constrained Time-To-Leave Prediction And In-Game Currency Price Generation
SupportVectorRegression	SVR	💾 🟡	Constrained Time-To-Leave Prediction And In-Game Currency Price Generation
SupportVectorRegressionGradientVariant	SVR	🟢 🟡	Real-Time Constrained Time-To-Leave Prediction And In-Game Currency Price Generation
KNearestNeighboursRegressor	KNN-R	🟢 🟡	Memory-Based Time-To-Leave Prediction And In-Game Currency Price Generation
NormalEquationLinearRegression*	None	💾 ⚡ 🟢 🟡 ⚠️	Instant Train Time-To-Leave Prediction And In-Game Currency Price Generation
BayesianLinearRegression*	None	💾 ⚡ 🟢 🟡 ⚠️	Instant Train Time-To-Leave Prediction And In-Game Currency Price Generation With Probability Estimation
BayesianQuantileLinearRegression*	None	💾 ⚡ 🟢 🟡 ⚠️	Instant Train Time-To-Leave Prediction And In-Game Currency Price Generation With Case Estimation

* The “instant train” models assumes that the features have a linear relationship with the label values, which is almost certainly not true in game-related settings.

Classification

Model	Alternate Names	Properties	Use Cases
BinaryRegression	Perceptron, Sigmoid Regression	🔰 🟢 🟡	Probability-To-Leave Prediction, Player Churn Prediction, Confidence Prediction
PassiveAggressiveClassifier	PA-C	⚡ 🟢	Fast Purchase Likelihood Estimation, Decision Making
NearestCentroid	NC	⚡ 🟢 🟡	Fast Grouping Or Quick Decision Making
KNearestNeighboursClassifier	KNN-C	🟢 🟡	Item Recommendation, Similar Player Matchmaking
SupportVectorMachine	SVM	💾 🟡	Boundary-Based Prediction
SupportVectorMachineGradientVariant	SVM	🟢 🟡	Real-Time Boundary-Based Prediction
NeuralNetwork	Multi-Layer Perceptron	🟢 🟡	Decision-Making, Player Behaviour Prediction
GaussianNaiveBayes*	GNB	💾 ⚡ 🟢 🟡 ⚠️	Enemy Data Generation, Player Behavior Categorization (e.g. Cautious Vs. Aggressive), Fast State Classification
MultinomialNaiveBayes*	MNB	💾 ⚡ 🟢 🟡 ⚠️	Summoning Next Enemy Type, Inventory Action Prediction, Strategy Profiling Based on Item Usage
BernoulliNaiveBayes*	BNB	💾 ⚡ 🟢 🟡 ⚠️	Binary Action Prediction (e.g. Jump Or Not), Quick Decision Filters
ComplementNaiveBayes*	CNB	💾 ⚡ 🟢 🟡 ⚠️	Imbalanced Class Prediction (e.g. Rare Choices, Rare Paths)
CategoricalNaiveBayes*	CNB	💾 ⚡ 🟢 🟡 ⚠️	Player Choice Prediction (e.g. Weapon Type, Character Class, Map Region Selection)
OrdinalRegression	Ordinal Classification	🟢 🟡 ⚠️	Skill Tier Prediction, Dynamic Difficulty Adjustment, Ranking Systems

* “Naive Bayes” models assumes that the features are independent to each other, which is almost certainly not true in game-related settings. Additionally, these models are better as generative models, despite being commonly taught as a classifier.

Clustering

Model	Alternate Names	Properties	Use Cases
KMeans	None	🔰 🟢 🟡	Maximizing Area-of-Effect Abilities, Maximizing Target Grouping
FuzzyCMeans	None	🟢 🟡	Overlapping Area-of-Effect Abilities, Overlapping Target Grouping
KMedoids	None	🟢 🟡	Player Grouping Based On Player Locations With Leader Identification
AgglomerativeHierarchical	None	🟢 🟡	Enemy Data Generation
ExpectationMaximization	EM	🟢 🟡	Hacking Detection, Anomaly Detection
MeanShift	None	🛡️ 🟢 🟡	Boss Spawn Location Search Based On Player Locations
AffinityPropagation	AP	🟡	Player Grouping
DensityBasedSpatialClusteringOfApplicationsWithNoise	DBSCAN	🛡️ 🟡	Density Grouping
OrderingPointsToIdentifyClusteringStructure	OPTICS	🛡️ 🟡	Density Grouping
BisectingCluster	None	⚡ 🟡	Slow To Quick Grouping

Deep Reinforcement Learning

Model	Alternate Names	Properties	Use Cases
DeepQLearning	Deep Q Network	💾 🟢	Best Self-Learning Player AIs, Best Recommendation Systems
DeepNStepQLearning	Deep N-Step Q Network	💾 🟢	Best Self-Learning Player AIs, Best Recommendation Systems
DeepDoubleQLearningV1	Double Deep Q Network (2010)	💾 🛡️ 🟢	Stable Best Self-Learning Player AIs, Best Recommendation Systems
DeepDoubleQLearningV2	Double Deep Q Network (2015)	💾 🛡️ 🟢	Stable Best Self-Learning Player AIs, Best Recommendation Systems
DeepClippedDoubleQLearning	Clipped Deep Double Q Network	💾 🛡️ 🟢	Stable Best Self-Learning Player AIs, Best Recommendation Systems
DeepStateActionRewardStateAction	Deep SARSA	🟢	Safe Self-Learning Player AIs, Safe Recommendation Systems
DeepNStepStateActionRewardStateAction	Deep N-Step SARSA	🟢	Safe Self-Learning Player AIs, Safe Recommendation Systems
DeepDoubleStateActionRewardStateActionV1	Double Deep SARSA	🛡️ 🟢	Stable Safe Self-Learning Player AIs, Safe Recommendation Systems
DeepDoubleStateActionRewardStateActionV2	Double Deep SARSA	🛡️ 🟢	Stable Safe Self-Learning Player AIs, Safe Recommendation Systems
DeepExpectedStateActionRewardStateAction	Deep Expected SARSA	🟢	Balanced Self-Learning Player AIs, Balanced Recommendation Systems
DeepNStepExpectedStateActionRewardStateAction	Deep N-Step Expected SARSA	🟢	Balanced Self-Learning Player AIs, Balanced Recommendation Systems
DeepDoubleExpectedStateActionRewardStateActionV1	Double Deep Expected SARSA	🛡️ 🟢	Stable Balanced Self-Learning Player AIs, Balanced Recommendation Systems
DeepDoubleExpectedStateActionRewardStateActionV2	Double Deep Expected SARSA	🛡️ 🟢	Stable Balanced Self-Learning Player AIs, Balanced Recommendation Systems
DeepMonteCarloControl	None	❗ 🟢	Online Self-Learning Player AIs
DeepOffPolicyMonteCarloControl	None	🟢	Offline Self-Learning Player AIs
DeepTemporalDifference	TD	🟢	Priority Systems
DeepREINFORCE	None	🟢	Reward-Based Self-Learning Player AIs
VanillaPolicyGradient	VPG	❗ 🟢	Baseline-Based Self-Learning Player AIs
ActorCritic	AC	🟢	Critic-Based Self-Learning Player AIs
AdvantageActorCritic	A2C	🟢	Advantage-Based Self-Learning Player AIs
TemporalDifferenceActorCritic	TD-AC	🟢	Bootsrapped Online Self-Learning Player AIs
ProximalPolicyOptimization	PPO	🟢	Industry-Grade And Research-Grade Self-Learning Player And Vehicle AIs
ProximalPolicyOptimizationClip	PPO-Clip	🟢	Industry-Grade And Research-Grade Self-Learning Player And Vehicle AIs
SoftActorCritic	SAC	💾 🛡️ 🟢	Self-Learning Vehicle AIs
DeepDeterministicPolicyGradient	DDPG	🟢 ⚙️	Self-Learning Vehicle AIs
TwinDelayedDeepDeterministicPolicyGradient	TD3	🟢 🛡️ ⚙️	Self-Learning Vehicle AIs

Tabular Reinforcement Learning

Model	Alternate Names	Properties	Use Cases
TabularQLearning	Q-Learning	🔰 💾 🟢	Best Self-Learning Grid AIs
TabularNStepQLearning	N-Step Q-Learning	🔰 💾 🟢	Best Self-Learning Grid AIs
TabularDoubleQLearningV1	Double Q-Learning (2010)	💾 🛡️ 🟢	Best Self-Learning Grid AIs
TabularDoubleQLearningV2	Double Q-Learning (2015)	💾 🛡️ 🟢	Best Self-Learning Grid AIs
TabularClippedDoubleQLearning	Clipped Double Q-Learning	💾 🛡️ 🟢	Best Self-Learning Grid AIs
TabularStateActionRewardStateAction	SARSA	🔰 🟢	Safe Self-Learning Grid AIs
TabularNStepStateActionRewardStateAction	N-Step SARSA	🔰 🟢	Safe Self-Learning Grid AIs
TabularDoubleStateActionRewardStateActionV1	Double SARSA	🛡️ 🟢	Safe Self-Learning Grid AIs
TabularDoubleStateActionRewardStateActionV2	Double SARSA	🛡️ 🟢	Safe Self-Learning Grid AIs
TabularExpectedStateActionRewardStateAction	Expected SARSA	🟢	Balanced Self-Learning Grid AIs
TabularNStepExpectedStateActionRewardStateAction	N-Step Expected SARSA	🟢	Balanced Self-Learning Grid AIs
TabularDoubleExpectedStateActionRewardStateActionV1	Double Expected SARSA	🛡️ 🟢	Balanced Self-Learning Grid AIs
TabularDoubleExpectedStateActionRewardStateActionV2	Double Expected SARSA	🛡️ 🟢	Balanced Self-Learning Grid AIs
TabularMonteCarloControl	MC	🟢	Online Self-Learning Grid AIs
TabularOffPolicyMonteCarloControl	Off-Policy MC	🟢	Offline Self-Learning Grid AIs
TabularTemporalDifference	TD	🟢	Priority Systems
TabularREINFORCE	None	🟢	Reward-Based Self-Learning Grid AIs

Sequence Modelling

Model	Alternate Names	Properties	Use Cases
Markov*	None	💾 🟢	Single Feature Player State Prediction
DynamicBayesianNetwork*	DBN	💾 🟢	Multiple Features Player State Prediction
ConditionalRandomField*	CRF	🟢	Multiple Features Player State Prediction

These are single step variants of the sequence models. Hence, it will not use or return sequence of values.

Filtering

Model	Alternate Names	Properties	Use Cases
KalmanFilter	KF	🟢 ⚠️	Linear Movement Anti-Cheat
ExtendedKalmanFilter	EKF	🟢 ⚙️	Non-Linear Movement Anti-Cheat
UnscentedKalmanFilter	UKF	💾 🟢 ⚙️	Non-Linear Movement Anti-Cheat
UnscentedKalmanFilter (DataPredict Variant)	UKF-DP	💾 🟢 ⚙️	Non-Linear Movement Anti-Cheat

Outlier Detection

Model	Alternate Names	Properties	Use Cases
OneClassSupportVectorMachine	OC-SVM	💾 🟡	Hacking Detection, Anomaly Detection (Using Single Class Data)
OneClassPassiveAggressiveClassifier	OC-PA-C	❗ ⚡ 🟢	Fast Hacking Detection, Anomaly Detection (Using Single Class Data)
LocalOutlierFactor	LOF	🟢 🟡	Score-Based Play-Time Milestone Detection
LocalOutlierProbability	LoOP	🟢 🟡	Probability-Based Play-Time Milestone Detection

Recommendation

Model	Alternate Names	Properties	Use Cases
FactorizationMachine	FM	🟢 🟡 🛡️	Cold-Start User-Item Recommendation
FactorizedPairwiseInteraction	None	🟢 🟡 🛡️	Cold-Start User-Item Recommendation
SimonFunkMatrixFactorization	Funk MF	🟢 🟡	Early Netflix-Style User-Item Recommendation
SimonFunkMatrixFactorizationWithBiases	Funk MF	🟢 🟡 🛡️	Early Netflix-Style User-Item Recommendation
TwoTower	None	🟢 🟡	YouTube-Style User-Item Recommendation

Generative

Model	Alternate Names	Properties	Use Cases
GenerativeAdversarialNetwork	GAN	🟢 🟡	Enemy Data Generation
ConditionalGenerativeAdversarialNetwork	CGAN	🟢 🟡	Conditional Enemy Data Generation
WassersteinGenerativeAdversarialNetwork	WGAN	🟢 🟡	Stable Enemy Data Generation
ConditionalWassersteinGenerativeAdversarialNetwork	CWGAN	🟢 🟡	Stable Conditional Enemy Data Generation

Feature-Class Containers

Model	Alternate Names	Properties	Use Cases
Table	Grid	⚡🟢 🟡	Quick Look Up

BaseModels

BaseModel

NaiveBayesBaseModel

GradientMethodBaseModel

IterativeMethodBaseModel

DeepReinforcementLearningBaseModel

DeepReinforcementLearningActorCriticBaseModel

TabularReinforcementLearningBaseModel

MatrixFactorizationBaseModel

GenerativeAdversarialNetworkBaseModel