Contenido del Curso
ML Introduction with scikit-learn
ML Introduction with scikit-learn
1. Machine Learning Concepts
2. Preprocessing Data with Scikit-learn
Scikit-learn ConceptsGetting Familiar with DatasetDealing with Missing ValuesChallenge: Imputing Missing ValuesOrdinalEncoderOne-Hot EncoderLabelEncoderChallenge: Encoding Categorical VariablesWhy Scale the Data?StandardScaler, MinMaxScaler, MaxAbsScalerChallenge: Scaling the FeaturesPreprocessing Summary
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.
Tarea
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
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.
Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones¿Todo estuvo claro?
¡Gracias por tus comentarios!
Sección 4. Capítulo 10
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.
Tarea
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
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.
Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones¿Todo estuvo claro?
¡Gracias por tus comentarios!
Sección 4. Capítulo 10
Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones