Feature Engineering with the help of Data Visualization

Feature Engineering with the help of Data Visualization

Why Feature Engineering is important? The features in your data will directly influence the accuracy of your model. The better features gives good accuracy on your test data.

Why Feature Engineering is important?

The features in your data will directly influence the accuracy of your model. The better features gives good accuracy on your test data. The better the features that you choose, the better the results you will achieve.

Feature Reduction In ML

The General view is adding more features increases the model/classifier’s performance.

Let us see why this is not the case:

  • Curse of Dimensionlaity:

Choosing more number of features will usually degrade classifier’s performance.

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CURSE OF DIMENSIONALITY

  • Limited training data
  • Limited Computational Resources
  • addition of irrelevant/unnecessary features leads to increase in computational costs and decrease in performance.

Feature Selection:

Having n initial features, it is possible to select (2^n ) combinations of features(2^n subsets are possible).

We just can’t go over all the possible (2^n) subsets.

Feature Selection is an optimization problem

  • Search the space of possible subsets.
  • Pick the one which best suits.

Evaluating the Feature Subset

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