Conteúdo do Curso
ML Introduction with scikit-learn
ML Introduction with scikit-learn
Create 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'.
Tarefa
- Encode the
y
variable using theLabelEncoder
. - Make a pipeline containing
ct
,SimpleImputer
,StandardScaler
, andKNeighborsClassifier
(in that order). - Train the
pipe
object.
Obrigado pelo seu feedback!
Create 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'.
Tarefa
- Encode the
y
variable using theLabelEncoder
. - Make a pipeline containing
ct
,SimpleImputer
,StandardScaler
, andKNeighborsClassifier
(in that order). - Train the
pipe
object.
Obrigado pelo seu feedback!
Create 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'.
Tarefa
- Encode the
y
variable using theLabelEncoder
. - Make a pipeline containing
ct
,SimpleImputer
,StandardScaler
, andKNeighborsClassifier
(in that order). - Train the
pipe
object.
Obrigado pelo seu feedback!
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'.
Tarefa
- Encode the
y
variable using theLabelEncoder
. - Make a pipeline containing
ct
,SimpleImputer
,StandardScaler
, andKNeighborsClassifier
(in that order). - Train the
pipe
object.