Parametric and Nonparametric Algorithm

What are the key differences, and when should each be used?

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This article will explain the difference between parametric and nonparametric machine learning methods. We need to learn a function that maps the input, which is a set of independent variables called X, to the output, which is a variable called Y.

Y = f(X) 

We need to fit a model to the data in order to get a close approximation of an unknown function. Most of the time, we don’t know how the unknown function works. Because of this, we might have to use different models or make some assumptions about the form of function f in order to build there. In general, this process could be parametric or non-parametric.

Parametric Machine Learning Algorithms :

Assumptions can make it easier to learn, but they can also limit what you can learn. Parametric machine learning algorithms are those that simplify the function to a known form.

Parametric machine learning algorithms are often also called linear machine learning algorithms because the assumed functional form is often a linear combination of the input variables. The problem is that the real unknown function underneath might not be a linear function like a line. It could be almost a line, and the input data might need to be changed in a small way for it to work right. Or it could have nothing to do with a line, in which case the assumption is wrong and the method will not work.

Examples of parametric machine learning algorithms comprise:

• Logistic Regression
• Linear Discriminant Analysis
• Perceptron
• Naïve Bayes
• Simple Neural Networks

Parametric Machine Learning Algorithms have many advantages:

• Simpler: These methods are easier to understand and interpret results.
• Less Data: They do not need as much data for training and can function effectively even if there is not a perfect match between the model and the data.
• Speed: As a result of its speed, parametric models can be learned from data relatively quickly.

The following are some drawbacks of parametric machine learning algorithms:

• Poor Fit: It is quite doubtful that the methods will match the underlying mapping function when put into reality.
• Constrained: By selecting a functional form, these methods are extremely limited to the form that has been provided.
• Limited Complexity: The approaches are better suited to dealing with problems of a less complex nature.

Nonparametric Machine Learning Algorithms :

Nonparametric machine learning algorithms are those that don’t make a lot of assumptions about the form of the mapping function when they’re developing models. They are free to learn any functional form from the training data because they do not make any assumptions about the data.

Nonparametric approaches are useful in situations in which the user possesses a large amount of data but little or no prior knowledge and when the user does not wish to place an excessive amount of emphasis on selecting the features that are most important.

The following are some examples of non-parametric methods:

• Decision Trees
• Support Vector Machines
• Naïve Bayes
• Neural Networks

The following are some of the advantages of using nonparametric machine learning algorithms:

• Performance: Can lead to models with improved capabilities for making predictions.
• Flexibility: Accomplished in fitting a huge number of functional forms
• Power: There are no assumptions made regarding the underlying function.

The following are some of the restrictions imposed on nonparametric machine learning algorithms:

• Overfitting: There is a greater chance that the training data will be too well fit, and it is harder to explain why certain predictions are made.
• Slower: Training takes much longer because there are so many more things to consider.
• More data: To accurately estimate the mapping function, you’ll need a lot more training data.

To summarize, parametric approaches involve extensive assumptions about the mapping of input variables to output variables, which means that they are faster to train but may not be as powerful as nonparametric methods. There are few or no assumptions about the target function in nonparametric approaches, hence they take a lot more data, are slower to train, and have a larger model complexity, but they can provide more powerful models.

I hope you find this article helpful and have learned some new things ❤

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Reference :

Master Machine Learning Algorithms by Jason Brownlee