Зміст курсу
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'.
Завдання
- Encode the
yvariable using theLabelEncoder. - Make a pipeline containing
ct,SimpleImputer,StandardScaler, andKNeighborsClassifier(in that order). - Train the
pipeobject.
Завдання
- Encode the
yvariable using theLabelEncoder. - Make a pipeline containing
ct,SimpleImputer,StandardScaler, andKNeighborsClassifier(in that order). - Train the
pipeobject.
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантівВсе було зрозуміло?
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'.
Завдання
- Encode the
yvariable using theLabelEncoder. - Make a pipeline containing
ct,SimpleImputer,StandardScaler, andKNeighborsClassifier(in that order). - Train the
pipeobject.
Завдання
- Encode the
yvariable using theLabelEncoder. - Make a pipeline containing
ct,SimpleImputer,StandardScaler, andKNeighborsClassifier(in that order). - Train the
pipeobject.
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантівВсе було зрозуміло?
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'.
Завдання
- Encode the
yvariable using theLabelEncoder. - Make a pipeline containing
ct,SimpleImputer,StandardScaler, andKNeighborsClassifier(in that order). - Train the
pipeobject.
Завдання
- Encode the
yvariable using theLabelEncoder. - Make a pipeline containing
ct,SimpleImputer,StandardScaler, andKNeighborsClassifier(in that order). - Train the
pipeobject.
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантівВсе було зрозуміло?
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'.
Завдання
- Encode the
yvariable using theLabelEncoder. - Make a pipeline containing
ct,SimpleImputer,StandardScaler, andKNeighborsClassifier(in that order). - Train the
pipeobject.
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантівПерейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів