Apr 20th, 2017
Most common steps towards creating artificial intelligence are -
Based on the understanding of the domain you are solving for and data knowledge, one is well equipped to select models that would work best. Some examples -
There are readily available algorithms for different modes of machine learning in different languages, platforms.Some examples -
Feature selection is the process of selecting a subset of relevant features for use in model construction. Feature selection is itself useful, but it mostly acts as a filter, muting out features that aren’t useful in addition to your existing features. Feature selection techniques are used for four reasons:
Feature selection is different from dimensionality reduction. Both methods seek to reduce the number of attributes in the dataset, but a dimensionality reduction method do so by creating new combinations of attributes, where as feature selection methods include and exclude attributes present in the data without changing them. Examples of dimensionality reduction methods include Principal Component Analysis, Singular Value Decomposition and Sammon’s Mapping.
Feature selection methods can be used to identify and remove unneeded, irrelevant and redundant attributes from data that do not contribute to the accuracy of a predictive model or may in fact decrease the accuracy of the model. Fewer attributes is desirable because it reduces the complexity of the model, and a simpler model is simpler to understand and explain.
The objective of feature selection is three-fold: