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Let’s face it, even before we were properly exposed to data science we had probably heard both of these terms: overfitting and underfitting. The reason these two terms shall be regarded as the guiding philosophy of machine learning is that every machine learning model in existence conforms to the trade-off between both of these, which in turn dictates their performance and therefore every machine learning algorithm seeks to create models that offer the best trade-off between them.

But why do we care about it?

Whenever we model any data using machine learning, the end objective is that the trained model should be able to correctly predict the…


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Data Scientist (n.): Person who is better at statistics than any software engineer and better at software engineering than any statistician.” -Josh Wills, Director of Data Engineering at Slack

We stand in midst of a deluge of data today. Starting from the smartphone in your palm to the smart refrigerator at your home, it’s everywhere. Today, over 2.5 quintillion bytes of data is generated every day, which is expected to rise up to 463 exabytes by 2025. Even though the systems that generate these vast volumes of data expire in a matter of time, the data doesn’t. …

Neelanjan Manna

Developing data-oriented solutions for everyone’s needs. B. Tech. CS Undergrad at KIIT University. Research Associate Intern at LCS2, IIIT-Delhi.

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