Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc... ...
Full BioNand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc...
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5 Things to Know About Machine Learning

- stratified generates random predictions by respecting the training set class distribution.
- most_frequent always predicts the most frequent label in the training set.
- prior always predicts the class that maximizes the class prior (like most_frequent`) and ``predict_proba returns the class prior.
- uniform generates predictions uniformly at random.
- constant always predicts a constant label that is provided by the user.
- the training set is used to train a given model
- the validation set is used to choose between models (for instance, does a random forest or a neural net work better for your problem? do you want a random forest with 40 trees or 50 trees?)
- the test set tells you how youâ??ve done. If youâ??ve tried out a lot of different models, you may get one that does well on your validation set just by chance, and having a test set helps make sure that is not the case.

Kaggle currently has a competition to predict the sales in a chain of Ecuadorian grocery stores. Kaggleâ??s â??training dataâ?? runs from Jan 1 2013 to Aug 15 2017 and the test data spans Aug 16 2017 to Aug 31 2017. A good approach would be to use Aug 1 to Aug 15 2017 as your validation set, and all the earlier data as your training set.
