There are many popular and powerful machine learning applications that are commonly used by the academia as well as in the industry.
While learning about different alogrithms for classification, we understand the strength and weakness of individual algorithms.
Choosing a appropriate algorithm for classificaion of a particular problem task requires a lot of practice, each algorithms has its own quirks and is based on certain assumptions. In practice, it is always recommended that we should compare the performance of different algorithms to select the best model for the particular problem.
These things vary in the number of features or samples, the noise in the dataset and whether the classes are linearily separable or not.
The performance of a classifier, computational performance as well as predictive power - depends heavily on the underlying data that is available for learning.
Here are the 7 Steps to develop a Machine Learning Application
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