Update Linear and Logistic Regression Parameters & Improve Documentation #8982
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- Add comprehensive documentation for supported: - Optimizers (SIMPLE_SGD, LINEAR_DECAY_SGD, etc.) - Objective types (ABSOLUTE_LOSS, HUBER, SQUARED_LOSS) - Momentum types (STANDARD, NESTEROV) - Fix parameter name typos Signed-off-by: rithin-pullela-aws <[email protected]>
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Corresponding code/ parameter names: https://github.com/opensearch-project/ml-commons/tree/main/common/src/main/java/org/opensearch/ml/common/input/parameter/regression |
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Thank you, @rithin-pullela-aws! Could we add some more information for the user about these options?
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| A classification algorithm, logistic regression models the probability of a discrete outcome given an input variable. In ML Commons, these classifications include both binary and multi-class. The most common is the binary classification, which takes two values, such as "true/false" or "yes/no", and predicts the outcome based on the values specified. Alternatively, a multi-class output can categorize different inputs based on type. This makes logistic regression most useful for situations where you are trying to determine how your inputs fit best into a specified category. | |||
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| **Optimisers supported:** `SIMPLE_SGD`, `LINEAR_DECAY_SGD`, `SQRT_DECAY_SGD`, `ADA_GRAD`, `ADA_DELTA`, `ADAM`, and `RMS_PROP`. | |||
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Could we add a description for each optimizer, objective, and momentum type so the user can choose appropriately?
| | `momentumType` | String | The Stochastic Gradient Descent (SGD) momentum that helps accelerate gradient vectors in the right direction, leading to faster convergence between vectors. | `STANDARD` | | ||
| | `optimizerType` | String | The optimizer used in the model. | `AdaGrad` | | ||
| | `decay_rate` | Double | The Root Mean Squared Propagation (RMSProp). | `0.9` | | ||
| | `momentum_type` | String | The Stochastic Gradient Descent (SGD) momentum that helps accelerate gradient vectors in the right direction, leading to faster convergence between vectors. | `STANDARD` | |
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| | `momentum_type` | String | The Stochastic Gradient Descent (SGD) momentum that helps accelerate gradient vectors in the right direction, leading to faster convergence between vectors. | `STANDARD` | | |
| | `momentum_type` | String | The Stochastic Gradient Descent (SGD) momentum that helps accelerate gradient vectors in the correct direction, leading to faster convergence between vectors. | `STANDARD` | |
| | `optimizerType` | String | The optimizer used in the model. | `AdaGrad` | | ||
| | `decay_rate` | Double | The Root Mean Squared Propagation (RMSProp). | `0.9` | | ||
| | `momentum_type` | String | The Stochastic Gradient Descent (SGD) momentum that helps accelerate gradient vectors in the right direction, leading to faster convergence between vectors. | `STANDARD` | | ||
| | `optimiser` | String | The optimizer used in the model. | `ADA_GRAD` | |
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Is this the correct parameter name spelling? The American spelling is "optimizer".
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Closes #8981
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