Models - Model Parameters Compatibility
This documentation shows you on which algorithms can be switched with others.
However, it assumes that you will ignore the difference in properties, hyperparameters and data distribution that the model trained on.
Full Compatibility
The algorithms under the same group can have its model parameters swapped without additional changes.
Closed-Form Linear Regression
- BayesianLinearRegression
- BayesianQuantileLinearRegression
Gradient-Based
- LinearRegression
- QuantileRegression
- PoissonRegression
- NegativeBinomialRegression
- GammaRegression
- SupportVectorRegressionGradientVariant
- PassiveAggressiveRegressor
- BinaryRegression
- SupportVectorMachineGradientVariant
- PassiveAggressiveClassifier
- OneClassPassiveAggressiveClassifier
Naive Bayes
- All except for CategoricalNaiveBayes
K-Nearest Neigbours
- All
Mean-Based With Data Point Number
- NearestCentroids
- KMeans
Deep Reinforcement Learning (Single)
- All
Deep Reinforcement Learning (Actor-Critic)
- All
Tabular Reinforcement Learning
- All
Generative Adversarial Network
- All
Outlier Detection
- All
Partial Compatibility
The algorithms under the same group can have its model parameters swapped with some changes to the model parameters. Refer to the models’ API reference for more information.
Statistical-Based Clustering
- MeanShift
- ExpectedMaximization
- FuzzyCMeans
Kalman Filters
- All