Challenge: 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.
Tehtävä
Swipe to start coding
- Encode the target.
- Split the data so that 33% is used for the test set and the remainder for the training set.
- Make a
ColumnTransformerto encode only the'island'and'sex'columns. Make sure the others columns remain untouched. Use a proper encoder for nominal data. - Fill the gaps in a
param_gridto try the following values for the number of neighbors:[1, 3, 5, 7, 9, 12, 15, 20, 25]. - Create a
GridSearchCVobject with theKNeighborsClassifieras a model. - Construct a pipeline that begins with
ctas the first step, followed by imputation using the most frequent value, standardization, and concludes withGridSearchCVas the final estimator. - Train the model using a pipeline on the training set.
- Evaluate the model on the test set. (Print its score)
- Get a predicted target for
X_test. - Print the best estimator found by
grid_search.
Ratkaisu
Oliko kaikki selvää?
Kiitos palautteestasi!
Osio 4. Luku 10
single
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Completion rate improved to 3.13Challenge: Putting It All Together
Pyyhkäise näyttääksesi valikon
In this challenge, you will apply everything you learned throughout the course from data preprocessing to training and evaluating the model.
Tehtävä
Swipe to start coding
- Encode the target.
- Split the data so that 33% is used for the test set and the remainder for the training set.
- Make a
ColumnTransformerto encode only the'island'and'sex'columns. Make sure the others columns remain untouched. Use a proper encoder for nominal data. - Fill the gaps in a
param_gridto try the following values for the number of neighbors:[1, 3, 5, 7, 9, 12, 15, 20, 25]. - Create a
GridSearchCVobject with theKNeighborsClassifieras a model. - Construct a pipeline that begins with
ctas the first step, followed by imputation using the most frequent value, standardization, and concludes withGridSearchCVas the final estimator. - Train the model using a pipeline on the training set.
- Evaluate the model on the test set. (Print its score)
- Get a predicted target for
X_test. - Print the best estimator found by
grid_search.
Ratkaisu
Vaihda työpöytään todellista harjoitusta vartenJatka siitä, missä olet käyttämällä jotakin alla olevista vaihtoehdoistaOliko kaikki selvää?
Kiitos palautteestasi!
Awesome!
Completion rate improved to 3.13Osio 4. Luku 10
single
