10 Machine Learning Algorithms You need to Know - Online Free Computer Tutorials.

'Software Development, Games Development, Mobile Development, iOS Development, Android Development, Window Phone Development. Dot Net, Window Services,WCF Services, Web Services, MVC, MySQL, SQL Server and Oracle Tutorials, Articles and their Resources

Tuesday, January 16, 2018

10 Machine Learning Algorithms You need to Know

In machine learning, there's something called the "No Free Lunch" theorem. In a nutshell, it states that no one algorithm works best for every problem, and it's especially relevant for supervised learning (i.e. predictive modeling).



For example, you can't say that neural networks are always better than decision trees or vice-versa. There are many factors at play, such as the size and structure of your dataset.



As a result, you should try many different algorithms for your problem, while using a hold-out "test set" of data to evaluate performance and select the winner.



Of course, the algorithms you try must be appropriate for your problem, which is where picking the right machine learning task comes in. As an analogy, if you need to clean your house, you might use a vacuum, a broom, or a mop, but you wouldn't bust out a shovel and start digging.


I guess you came to this post by searching similar kind of issues in any of the search engine and hope that this resolved your problem. If you find this tips useful, just drop a line below and share the link to others and who knows they might find it useful too.

Stay tuned to my blogtwitter or facebook to read more articles, tutorials, news, tips & tricks on various technology fields. Also Subscribe to our Newsletter with your Email ID to keep you updated on latest posts. We will send newsletter to your registered email address. We will not share your email address to anybody as we respect privacy.


This article is related to

Machine learning,Algorithms for Machine Learning,The Big Principle,Linear Regression,Logistic Regression,Linear Discriminant Analysis,Classification and Regression Trees,Naive Bayes,K-Nearest Neighbors,Learning Vector Quantization,Support Vector Machines, Bagging and Random Forest,Boosting and AdaBoost,

No comments:

Post a Comment