Challanges in Machine Learning Flashcards

1
Q

How can the problem of irrelevant features be solved?

A
  • Remove features
  • Combine features
  • Gather data to create new features
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2
Q

How can the problem of overfitting be fixed?

A
  • Gather more data
  • Simplify the model
  • Reduce training set noise
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3
Q

What are some ways an ML model can be simplified?

A
  • Using a simpler algorithm
  • Reducing the number of model parameters being used
  • Regularizing the model
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4
Q

How can the problem of under-fitting be solved?

A
  • Use a more complex model
  • Feature engineering
  • Reduce the number of learning constraints
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5
Q

How can error in the training set be rectified?

A
  • Remove outliers

- Find missing features

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6
Q

How is the problem of data mismatch solved?

A

Separating the mismatched data into a seperate data set.

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7
Q

How can you use the train-dev set to discover overfitting?

A

If the model does well on the test set, but not the train-dev set.

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8
Q

How you use the train-dev set to discover mismatched data in the training set?

A

Model does well on training and train-dev set, but not the validation set.

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