Lernen 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.

Aufgabe

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 ColumnTransformer to 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_grid to try the following values for the number of neighbors: [1, 3, 5, 7, 9, 12, 15, 20, 25].
Create a GridSearchCV object with the KNeighborsClassifier as a model.
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.
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.

Wechseln Sie zum Desktop, um in der realen Welt zu übenFahren Sie dort fort, wo Sie sind, indem Sie eine der folgenden Optionen verwenden

War alles klar?

Danke für Ihr Feedback!

Abschnitt 4. Kapitel 10

single

Fragen Sie AI

Fragen Sie alles oder probieren Sie eine der vorgeschlagenen Fragen, um unser Gespräch zu beginnen

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

Aufgabe

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 ColumnTransformer to 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_grid to try the following values for the number of neighbors: [1, 3, 5, 7, 9, 12, 15, 20, 25].
Create a GridSearchCV object with the KNeighborsClassifier as a model.
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.
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.

Wechseln Sie zum Desktop, um in der realen Welt zu übenFahren Sie dort fort, wo Sie sind, indem Sie eine der folgenden Optionen verwenden

War alles klar?

Danke für Ihr Feedback!

Abschnitt 4. Kapitel 10

single

Swipe um das Menü anzuzeigen

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

Aufgabe

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 ColumnTransformer to 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_grid to try the following values for the number of neighbors: [1, 3, 5, 7, 9, 12, 15, 20, 25].
Create a GridSearchCV object with the KNeighborsClassifier as a model.
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.
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.

Wechseln Sie zum Desktop, um in der realen Welt zu übenFahren Sie dort fort, wo Sie sind, indem Sie eine der folgenden Optionen verwenden

War alles klar?

Danke für Ihr Feedback!

Challenge: Putting It All Together