Classification in Depth with Scikit-Learn

# Quiz Time: How Much Do You Know?

In this project you will be asked to complete some minor challenges to test your knowledge of Classification.
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## Project Activities

All our Data Science projects include bite-sized activities to test your knowledge and practice in an environment with constant feedback.

All our activities include solutions with explanations on how they work and why we chose them.

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### We build a model and test it on a set of 100 customer records, and the resulting confusion matrix is as follows:

Predicted Negative Predicted Positive
Actual Negative 70 10
Actual Positive 5 15

Compute the precision score to one decimal place

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### We build a model and test it on a set of 100 customer records, and the resulting confusion matrix is as follows:

Predicted Negative Predicted Positive
Actual Negative 80 5
Actual Positive 5 20

Compute the recall score to one decimal place

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### As a reviewer for an international conference, you have been given papers with different experimental setups to review. Based on the content of each paper, would you recommend accepting or rejecting them?

In comparison to your algorithm, mine appears to be more effective. I suggest observing the training error rates for confirmation.

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### Let´s suppose three classifications model are built to discriminate apples from bananas. The following table shows the results obtained according to these algorithms:

model Model 1 Model 2 Model 3
Accuracy (training) 0.99 0.93 0.99
Accuracy (testing) 0.90 0.75 0.10
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#### Verónica Barraza

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## Classification in Depth with Scikit-Learn

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