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ML Introduction with scikit-learn

bookChallenge: Putting It All Together

In this challenge, you will apply everything you learned throughout the course from data preprocessing to training and evaluating the model.

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Task

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  1. Encode the target.
  2. Split the data so that 33% is used for the test set and the remainder for the training set.
  3. Make a ColumnTransformer to encode only the 'island' and 'sex' columns. Make sure the others columns remain untouched. Use a proper encoder for nominal data.
  4. Fill the gaps in a param_grid to try the following values for the number of neighbors: [1, 3, 5, 7, 9, 12, 15, 20, 25].
  5. Create a GridSearchCV object with the KNeighborsClassifier as a model.
  6. Construct a pipeline that begins with ct as the first step, followed by imputation using the most frequent value, standardization, and concludes with GridSearchCV as the final estimator.
  7. Train the model using a pipeline on the training set.
  8. Evaluate the model on the test set. (Print its score)
  9. Get a predicted target for X_test.
  10. Print the best estimator found by grid_search.

Solution

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SectionΒ 4. ChapterΒ 10
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bookChallenge: Putting It All Together

Swipe to show menu

In this challenge, you will apply everything you learned throughout the course from data preprocessing to training and evaluating the model.

carousel-imgcarousel-imgcarousel-imgcarousel-imgcarousel-img
Task

Swipe to start coding

  1. Encode the target.
  2. Split the data so that 33% is used for the test set and the remainder for the training set.
  3. Make a ColumnTransformer to encode only the 'island' and 'sex' columns. Make sure the others columns remain untouched. Use a proper encoder for nominal data.
  4. Fill the gaps in a param_grid to try the following values for the number of neighbors: [1, 3, 5, 7, 9, 12, 15, 20, 25].
  5. Create a GridSearchCV object with the KNeighborsClassifier as a model.
  6. Construct a pipeline that begins with ct as the first step, followed by imputation using the most frequent value, standardization, and concludes with GridSearchCV as the final estimator.
  7. Train the model using a pipeline on the training set.
  8. Evaluate the model on the test set. (Print its score)
  9. Get a predicted target for X_test.
  10. Print the best estimator found by grid_search.

Solution

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Everything was clear?

How can we improve it?

Thanks for your feedback!

close

Awesome!

Completion rate improved to 3.13
SectionΒ 4. ChapterΒ 10
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