What is the capacity of a machine learning model?

What is the capacity of a machine learning model?

Conceptually, Capacity represents the number of functions that a machine learning model can select as a possible solution. for instance, la linear regression model can have all degree 1 polynomials of the form y = w*x + b as a Capacity (meaning all the potential solutions).

What is capacity in deep learning?

The capacity of a network refers to the range or scope of the types of functions that the model can approximate. Informally, a model’s capacity is its ability to fit a wide variety of functions. — Pages 111-112, Deep Learning, 2016. A model with less capacity may not be able to sufficiently learn the training dataset.

What are the most important machine learning algorithms?

Top Machine Learning Algorithms You Should Know

  • Linear Regression.
  • Logistic Regression.
  • Linear Discriminant Analysis.
  • Classification and Regression Trees.
  • Naive Bayes.
  • K-Nearest Neighbors (KNN)
  • Learning Vector Quantization (LVQ)
  • Support Vector Machines (SVM)

How do we quantify model capacity?

The most common way to estimate the capacity of a model is to count the number of parameters. The more parameters, the higher the capacity in general. Of course, often a smaller network learns to model more complex data better than a larger network, so this measure is also far from perfect.

What is regularization in data science?

Regularization is a technique used for tuning the function by adding an additional penalty term in the error function. The additional term controls the excessively fluctuating function such that the coefficients don’t take extreme values.

How to control the capacity of a learning algorithm?

•  When model has higher capacity, it overfits – One way to control capacity of a learning algorithm is by choosing the hypothesis space •  i.e., set of functions that the learning algorithm is allowed to select as being the solution

How is representation learning used in machine learning?

One of the most exciting threads of representation learning in recent years has been learning feature representations which could be fed into standard machine learning (usually supervised learning) algorithms. Depending on the intended learning algorithm, the representation has to support some set of operations. For instance,

What does capacity mean in a machine learning model?

•  Model capacity is ability to fit variety of functions – Model with Low capacitystruggles to fit training set – A High capacitymodel can overfit by memorizing properties of training set not useful on test set •  When model has higher capacity, it overfits

Which is the best measure of representational capacity?

The most popular measure of representational capacity is the V C Dimension of a model. The upper bound for VC dimension ( d) of a model is: where | H | is the cardinality of the set of hypothesis space.