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Create a Complete ML Pipeline | Pipelines
ML Introduction with scikit-learn
course content

Contenido del Curso

ML Introduction with scikit-learn

ML Introduction with scikit-learn

1. Machine Learning Concepts
2. Preprocessing Data with Scikit-learn
3. Pipelines
4. Modeling

bookCreate a Complete ML Pipeline

Now let's build a proper pipeline with the final estimator. As a result, we will get a trained prediction pipeline that can be used for predicting new instances simply by calling the .predict() method.
To train a predictor (model), you need y to be encoded. This is done separately from the Pipeline we build for X.
Remember that LabelEncoder is used for encoding the target.

You have the same Penguins dataset.
The task is to build a pipeline with KNeighborsClassifier as a final estimator, train it, and predict values for the X itself.
Since the predictions will be encoded (0, 1, or 2), we will use the .inverse_transform() method of LabelEncoder to get the predictions back to 'Adelie', 'Chinstrap', or 'Gentoo'.

Tarea

  1. Encode the y variable using the LabelEncoder.
  2. Make a pipeline containing ct, SimpleImputer, StandardScaler, and KNeighborsClassifier (in that order).
  3. Train the pipe object.

Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

Sección 3. Capítulo 6
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bookCreate a Complete ML Pipeline

Now let's build a proper pipeline with the final estimator. As a result, we will get a trained prediction pipeline that can be used for predicting new instances simply by calling the .predict() method.
To train a predictor (model), you need y to be encoded. This is done separately from the Pipeline we build for X.
Remember that LabelEncoder is used for encoding the target.

You have the same Penguins dataset.
The task is to build a pipeline with KNeighborsClassifier as a final estimator, train it, and predict values for the X itself.
Since the predictions will be encoded (0, 1, or 2), we will use the .inverse_transform() method of LabelEncoder to get the predictions back to 'Adelie', 'Chinstrap', or 'Gentoo'.

Tarea

  1. Encode the y variable using the LabelEncoder.
  2. Make a pipeline containing ct, SimpleImputer, StandardScaler, and KNeighborsClassifier (in that order).
  3. Train the pipe object.

Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

Sección 3. Capítulo 6
toggle bottom row

bookCreate a Complete ML Pipeline

Now let's build a proper pipeline with the final estimator. As a result, we will get a trained prediction pipeline that can be used for predicting new instances simply by calling the .predict() method.
To train a predictor (model), you need y to be encoded. This is done separately from the Pipeline we build for X.
Remember that LabelEncoder is used for encoding the target.

You have the same Penguins dataset.
The task is to build a pipeline with KNeighborsClassifier as a final estimator, train it, and predict values for the X itself.
Since the predictions will be encoded (0, 1, or 2), we will use the .inverse_transform() method of LabelEncoder to get the predictions back to 'Adelie', 'Chinstrap', or 'Gentoo'.

Tarea

  1. Encode the y variable using the LabelEncoder.
  2. Make a pipeline containing ct, SimpleImputer, StandardScaler, and KNeighborsClassifier (in that order).
  3. Train the pipe object.

Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

Now let's build a proper pipeline with the final estimator. As a result, we will get a trained prediction pipeline that can be used for predicting new instances simply by calling the .predict() method.
To train a predictor (model), you need y to be encoded. This is done separately from the Pipeline we build for X.
Remember that LabelEncoder is used for encoding the target.

You have the same Penguins dataset.
The task is to build a pipeline with KNeighborsClassifier as a final estimator, train it, and predict values for the X itself.
Since the predictions will be encoded (0, 1, or 2), we will use the .inverse_transform() method of LabelEncoder to get the predictions back to 'Adelie', 'Chinstrap', or 'Gentoo'.

Tarea

  1. Encode the y variable using the LabelEncoder.
  2. Make a pipeline containing ct, SimpleImputer, StandardScaler, and KNeighborsClassifier (in that order).
  3. Train the pipe object.

Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
Sección 3. Capítulo 6
Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
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