parametric models and nonparametric models

Strengths of Parametric Modeling:

  • Parametric models are suitable for scenarios where there is already an explicit relationship between the input variables and the predicted target.
  • These models require some prior knowledge of the problem and are suitable for simple, low-dimensional datasets.

Flexibility of non-parametric models:

  • Non-parametric machine learning algorithms are able to automatically discover complex trends and structures in data.
  • They do not require an explicitly specified model and can learn data distributions without a priori assumptions.
  • These models are suitable for high-dimensional, complex real-world datasets and can achieve high accuracy rates.

Examples of non-parametric models:

  • Common non-parametric models include:
  • K-Nearest Neighbors (KNN).
  • Support Vector Machines, SVM
  • Decision Trees (DT).
  • Ensemble Methods such as Random Forests and Gradient Boosting Trees.

Parametric vs. non-parametric models:

  • Parametric models:
  • Have a fixed number of parameters, e.g., linear regression and logistic regression.
  • Stronger dependence on model assumptions (e.g., data are linearly separable).
  • Advantage is computationally efficient and suitable for small data sets or clear theoretical models.
  • Non-parametric models:
  • The number of parameters is not fixed and can be adjusted with data complexity.
  • More suitable for dealing with nonlinear relationships and complex distributions.
  • Disadvantages are high computational complexity and may require a larger amount of training data.

Advantages of non-parametric models:

  • Automatic pattern discovery: no need to artificially define data distributions or assumptions, the model can adapt itself to the data.
  • Expressive: Captures complex relationships between high-dimensional features.
  • Strong elasticity: well adapted to a wide range of data types, including classification, regression and sorting problems.

Typical application scenarios:

  • KNN: suitable for classification problems, such as recommendation system, image recognition.
  • SVM: Handles high-dimensional spatial classification tasks, e.g., text categorization, bioinformatics.
  • Decision trees and integration methods: widely used in regression, classification and anomaly detection tasks

Challenges in Machine Learning