Distributed training using Scikit-learn and Python


Machine learning is a really distributed and practical field. For learning mL, you need to learn so many things Scikit-learn and python are one of them. It includes data analyzation, cleaning, plotting, training and then testing. We use the distributed method of machine learning to increase the performance of model in case of large scale data-sets. Here we can use more effective methods to reduce learning errors. It is also better for testing purposes. It includes some important and fixed steps which are designing a fast algorithm, some relation representation and then partition and training of data. Well, it carries a lot of advantages with its cost and time-saving is one of them also it ensures the security and maintenance of data.

Some particular methods with Scikit-learn:

Well, this with Scikit learn is not only makes it easy but makes it sufficient as well. For example, we combine polynomial regression and linear regression for better results. Sometimes we also apply ridge, lasso, elastic-net. Even we use different types of classifications in SVM to train the model for better results. For example soft margin classification and linear SVM classification, non-linear classification and polynomial kernel.



  • It provides a natural solution for large scale data sets.
  • This decreases the chance of insufficient and incorrect statistics.
  • Multicore processors can perform different operations on different parts of data
  • It is scalable just because of the growing size of data day by day.


  • No restriction can be a major problem for some particular algorithms for example decision trees and neural networks.
  • Combining learning algorithms can be a problem because of different representations.
  • Sometimes defining distribution can be harder because of the distribution of data
  • It is difficult to define a particular uniform framework.
  • Not a good step to use it in small scale data.


Well, Distributed learning is all about training a data-set with a combination of algorithms, dividing a large scale data-set and distribute it. It is having so many advantages for large scale dataset but when we talk about small scale datasets, it will make them more complex and hard to train. But the main point is it takes care of privacy of data and reduces the cost a lot.

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