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Machine learning models can be parameterized in various ways, and the choice of parameterization depends on the specific model and the problem you are trying to solve. There is no fixed number of parameterizations in machine learning, as the field is constantly evolving, and researchers continue to develop new techniques and approaches. However, I can provide an overview of some common types of parameterizations in machine learning:

1. Linear Models:

   • Linear regression: Parameters include coefficients for each feature and an intercept.
   • Logistic regression: Similar to linear regression but used for binary classification.
2. Neural Networks:
   • Feedforward neural networks: Parameters include weights and biases for each layer.
   • Convolutional neural networks (CNNs): Parameters include convolutional kernels, weights, and biases.
   • Recurrent neural networks (RNNs): Parameters include weights and biases for recurrent layers.
   • Transformers: Parameters include self-attention weights and feedforward neural network weights.
3. Support Vector Machines (SVM): Parameters include support vectors and kernel parameters.
4. Decision Trees: Parameters include split criteria and feature thresholds.
5. Random Forests: Parameters include multiple decision trees and their individual parameters.
6. k-Nearest Neighbors (k-NN): Parameters include the number of neighbors (k) and distance metric.
7. Clustering Algorithms: Parameters include cluster centroids and linkage criteria (e.g., K-means, hierarchical clustering).
8. Principal Component Analysis (PCA): Parameters include principal components and eigenvalues.
9. Gaussian Mixture Models (GMM): Parameters include mean vectors, covariance matrices, and mixture weights.
10. Hyperparameters: These are not model parameters but rather settings that control the learning process, such as learning rates, batch sizes, regularization strengths, etc.
11. GLM framework includes several parameterizations, primarily based on the choice of the probability distribution and the link function, including three important parameters: model parameters, natural parameters, and canonical parameters.

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