Machine Learning: Supervised Learning 3
Bayesian Network
Bayesian Network is a type of probabilistic graphical model that uses Bayesian inference for probability computation. Representation of casual relations makes the representation and inference efficient.
Bayesian Network
It is a graphical model that efficiently encodes the joint probability distribution for a large set of variables. Arcs represent probabilistic dependence on variables. Lack of an arc represents independence. The network structure is a directed acyclic graph.
Support Vector Machines
It is a machine learning algorithm that is used in classification and regression. But it is mostly used in classification problems. What it basically does is, it plots each data item as a point in n-dimensional space with values of each feature being values of a particular point and then separates the classes using a hyper-plane.
The line that separates the classes is determined by a subset of points know as Support Vectors.
Points with a circle around them are support vectors
Larger the functional margin leads to better confidence in the prediction.
What if the data has noise or is not linearly separable?
For noise, we introduce a value "C" which is the distance of error points to their correct zones. It controls the relative importance of maximizing the margin and fitting the training data.
Using a higher value of C during implementation will make the model try to adjust itself to more number of noisy data while a lower value will make it more strict.
For non-linearly separable data points we have kernels. Original input variables are mapped to a new set of input features via feature mapping. There are various types of kernel functions that are used:
1. Linear 2. Polynomial of power p 3. Gaussian (Radial basis function) 4.Sigmoid.
Kernel functions can be thought of a similarity measure between input objects.
EXAMPLES:
That's it for the supervised learning part. The next posts will be on Unsupervised learning.
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