Conteúdo do Curso
ML Introduction with scikit-learn
ML Introduction with scikit-learn
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.
Swipe to show code editor
- 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 theKNeighborsClassifier
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 withGridSearchCV
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
.
Obrigado pelo seu feedback!
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.
Swipe to show code editor
- 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 theKNeighborsClassifier
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 withGridSearchCV
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
.
Obrigado pelo seu feedback!