Linear Learner in SageMaker: Hyperparameter Tuning

Balance_multiclass_weights: This hyperparameter becomes relevant for multi-class classification problems. It allows you to specify weights for each class, addressing imbalanced datasets where certain classes have significantly fewer examples. Assigning higher weights to under-represented classes helps the model focus on learning from those examples more effectively.

Learning_rate: This hyperparameter controls the step size taken by the optimizer during model training. A larger learning rate can lead to faster convergence but might cause the model to overshoot the optimal solution. Conversely, a smaller learning rate can lead to slower convergence but might result in a more stable model. Finding the right balance is crucial.

mini_batch_size: This hyperparameter defines the number of data points processed by the model in each training iteration (epoch). A larger batch size can improve training efficiency but might lead to poorer model generalization. A smaller batch size can improve generalization but might slow down training. Experimentation is key to finding the optimal value for your specific dataset.

L1 (LASSO): L1 regularization penalizes the model for the absolute value of its coefficients (feature weights). This can lead to sparse models where some feature weights become zero, effectively removing those features from the model. This helps in reducing model complexity and potentially improving generalization.

wd (Weight Decay): Similar to L1, weight decay (L2 regularization) penalizes the model for the squared value of its coefficients. This discourages large weights and promotes smoother decision boundaries, potentially reducing overfitting.

target_precision: This hyperparameter can be used for binary classification problems where you want to prioritize a specific class (positive class) in terms of precision. Precision refers to the proportion of predicted positives that are actually true positives. Setting a target precision value can guide the model to learn a decision boundary that ensures a certain level of precision for the positive class.

target_recall: Similar to target_precision, but for recall. Recall refers to the proportion of actual positives that are correctly identified by the model. Setting a target recall value can guide the model to learn a decision boundary that ensures a certain level of recall for the positive class.