## 3 Results
SdcaLogisticRegressionOva (One-vs-All): * Architecture: linear binary classifiers are built for each class in a one-vs-all setup using stochastic dual coordinate ascent (SDCA) with logistic regression; * Input parameters: each input feature contributes to the decision boundary; * Trainable parameters: based on the number of features and classes; * Dataset size: suitable for large datasets, especially when the number of classes is moderate; * Hyperparameters: * L2 Regularization: L2 regularization parameter for complexity control; * MaximumNumberOfIterations: sets the maximum number of iterations; * ConvergenceTolerance: convergence threshold for the optimizer;
3. SdcaMaximumEntropyMulti * Architecture: multiclass logistic regression is performed using SDCA with maximum entropy (cross-entropy loss); * Input parameters: each feature affects the class probabilities; * Trainable parameters: determined by the number of features and classes; * Dataset Size: works well with large datasets with balanced classes; * Hyperparameters: * L2 Regularization: regularization parameter for complexity control; * MaximumNumberOfIterations: maximum rounds of optimization (default \(\sim\)100); * ConvergenceTolerance: determines when to stop training;
4. LightGbmMulti: * Architecture: gradient-boosting trees (LightGBM) for multiclass problems; * Input parameters: each input feature influences the gradient boosting trees; * Trainable parameters: number of trees, leaves per tree, and depth contribute to total parameters; * Dataset size: performs well on large datasets; * Hyperparameters: * NumberOfLeaves: controls the number of terminal nodes in each tree; * NumberOfIterations: number of boosting rounds; * LearningRate: step size for each boosting iteration; * MinDataInLeaf: minimum samples per leaf for each tree;
5. LbfgsLogisticRegressionOva: * Architecture: logistic regression with L-BFGS optimization using a one-vs-all scheme; * Input parameters: linear combination of input features; * Trainable parameters: defined by the number of classes and features; * Dataset size: ideal for moderately large datasets; avoids overfitting in smaller ones; * Hyperparameters: * L2 Regularization: regularization term for the weights; * MaximumNumberOfIterations: sets a cap on the optimization rounds; * ConvergenceTolerance: stopping criteria threshold;
6. FastForestRegression: * Architecture: ensemble of decision trees trained in a random forest configuration for regression; * Input parameters: each feature affects the decision boundaries in the trees; * Trainable parameters: based on the number of trees and leaves; * Dataset size: effective with medium to large datasets; * Hyperparameters:\(\bigcirc\) NumberOffrees: controls ensemble size (default \(\sim\)100); \(\bigcirc\) NumberOfLeaves: maximum leaves in each tree; \(\bigcirc\) MinimumExampleCountPerLeaf: minimum samples required in each leaf;
7. SdcaRegression: \(\bullet\) Architecture: linear regression using stochastic dual coordinate ascent (SDCA); \(\bullet\) Input parameters: each input feature is linearly combined to produce the output; \(\bullet\) Trainable parameters: defined by the number of features; \(\bullet\) Dataset size: effective on large, high-dimensional datasets; \(\bullet\) Hyperparameters: \(\bigcirc\) L2 Regularization: regularization strength; \(\bigcirc\) MaximumNumberOfIterations: maximum iteration count; \(\bigcirc\) ConvergenceTolerance: threshold to determine convergence;
8. LbfgsPoissonRegressionRegression  
\(\bullet\) Architecture: Poisson regression optimized with L-BFGS, often used for count data; \(\bullet\) Input parameters: linear combination of input features affecting the Poisson model; \(\bullet\) Trainable parameters: based on the number of features; \(\bullet\) Dataset size: performs well with medium-sized datasets; \(\bullet\) Hyperparameters: \(\bigcirc\) L2 Regularization: regularization parameter to control complexity; \(\bigcirc\) MaximumNumberOfIterations: sets iteration cap; \(\bigcirc\) ConvergenceTolerance: determines when training should stop;
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