Parametric and non-parametric machine learning algorithms differ in their approach to modeling and handling data. Here are the key distinctions between the two:

Model Complexity:

Parametric: These algorithms make assumptions about the underlying data distribution and have a fixed number of parameters to learn from the data. Common examples include linear regression and logistic regression. Once the parameters are learned, the model is fully defined. Non-parametric: These algorithms do not make strong assumptions about the underlying data distribution and have a flexible number of parameters that can grow with the size of the training data. Examples include decision trees, k-nearest neighbors (KNN), and support vector machines (SVM). Memory Usage:

Parametric: Typically, parametric models have a fixed number of parameters, which makes them more memory-efficient, especially when dealing with large datasets. Non-parametric: These models often require more memory since they need to store information about the entire training dataset or its structure. Training Time:

Parametric: Training parametric models is generally faster because they have a fixed number of parameters to estimate. The training process involves optimizing these parameters to fit the data. Non-parametric: Training non-parametric models can be computationally more intensive, especially as the dataset size increases, since they need to consider the entire dataset or its structure during training. Flexibility:

Parametric: These models are less flexible in capturing complex patterns in the data. They may struggle with capturing intricate relationships unless the underlying assumptions are met. Non-parametric: Non-parametric models are more flexible and can adapt to complex relationships in the data. They don't rely on strict assumptions about the data distribution. Generalization:

Parametric: These models may generalize well if the underlying assumptions are met, but they might struggle if the assumptions are violated. Non-parametric: Non-parametric models tend to be more robust in capturing complex patterns and may generalize better in situations where the underlying assumptions of parametric models are not satisfied. In summary, parametric models make strong assumptions about the data and have a fixed number of parameters, while non-parametric models are more flexible, adapt to complex patterns, but may require more computational resources. The choice between the two depends on the characteristics of the data and the modeling goals.

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