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  1. With more variables, data visualization becomes difficult.
  2. All the variables might not be important for a particular business problem.
  3. More complex models as the model tries to learn from all of the variables, with more computation time.
  4. Exploratory Data Analysis becomes difficult.


Understanding how to think statistically by discussing two examples.

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GALLUP POLL, SEPTEMBER 2015


Part 2 in a series exploring different methods to evaluate machine learning models.

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     +--------------+-----------------+----------------------+
| Actual Value | Predicted Value | Error…


Exploring different methods to evaluate Machine Learning models for classification problems.

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Confusion Matrix

Shubham Dhingra

Continuous improvement is better than delayed perfection. -MT

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