Opened 18 months ago

Last modified 10 months ago

#7330 closed defect

Data Science Training in Chennai — at Initial Version

Reported by: aliyamanasa Assignee:
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Combining predictive models to make them collectively more efficient is an idea that, in large part, came to data scientists. Packaging and reinforcement approaches are often presented. They are based on the principle of applying the same learning algorithm in different data variants (for example, weighting observations) to obtain a set of classifiers with satisfactory heterogeneity.

The decomposition of bias varies essentially decomposes the learning error of any algorithm by adding the bias, variance and some irreducible error due to noise in the underlying data set. Essentially, if you make the model more complex and add more variables, you will lose the prejudice, but you will gain some variations. To obtain the ideally reduced amount of errors, you will have to compensate for the bias and variation. You do not want high tendency or high variation in your model. Many of the modern technologies are based on computational models known as artificial neural networks. Deep learning is becoming especially exciting now, as we have more data and larger neural networks to work with.


In addition, the performance of neural networks improves as they grow and work with more and more data, unlike other machine learning algorithms that can reach a level after a point. It goes without saying that we face many challenges in the analysis and study. of such a large volume of data with traditional data processing tools. To overcome these challenges, some great date solutions were introduced, such as Hadoop. These great date tools really helped to make the big date applications. More and more organizations, large and small, are taking advantage of the benefits provided by large data applications.

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