Regressor
Bases: ABC
Abstract base class for all regressors.
Source code in src/safeds/ml/classical/regression/_regressor.py
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fit(training_set)
abstractmethod
¶
Create a copy of this regressor and fit it with the given training data.
This regressor is not modified.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
training_set |
TaggedTable
|
The training data containing the feature and target vectors. |
required |
Returns:
Name | Type | Description |
---|---|---|
fitted_regressor |
Regressor
|
The fitted regressor. |
Raises:
Type | Description |
---|---|
LearningError
|
If the training data contains invalid values or if the training failed. |
Source code in src/safeds/ml/classical/regression/_regressor.py
is_fitted()
abstractmethod
¶
Check if the classifier is fitted.
Returns:
Name | Type | Description |
---|---|---|
is_fitted |
bool
|
Whether the regressor is fitted. |
mean_absolute_error(validation_or_test_set)
¶
Compute the mean absolute error (MAE) of the regressor on the given data.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
validation_or_test_set |
TaggedTable
|
The validation or test set. |
required |
Returns:
Name | Type | Description |
---|---|---|
mean_absolute_error |
float
|
The calculated mean absolute error (the average of the distance of each individual row). |
Raises:
Type | Description |
---|---|
UntaggedTableError
|
If the table is untagged. |
Source code in src/safeds/ml/classical/regression/_regressor.py
mean_squared_error(validation_or_test_set)
¶
Compute the mean squared error (MSE) on the given data.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
validation_or_test_set |
TaggedTable
|
The validation or test set. |
required |
Returns:
Name | Type | Description |
---|---|---|
mean_squared_error |
float
|
The calculated mean squared error (the average of the distance of each individual row squared). |
Raises:
Type | Description |
---|---|
UntaggedTableError
|
If the table is untagged. |
Source code in src/safeds/ml/classical/regression/_regressor.py
predict(dataset)
abstractmethod
¶
Predict a target vector using a dataset containing feature vectors. The model has to be trained first.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
dataset |
Table
|
The dataset containing the feature vectors. |
required |
Returns:
Name | Type | Description |
---|---|---|
table |
TaggedTable
|
A dataset containing the given feature vectors and the predicted target vector. |
Raises:
Type | Description |
---|---|
ModelNotFittedError
|
If the model has not been fitted yet. |
DatasetContainsTargetError
|
If the dataset contains the target column already. |
DatasetMissesFeaturesError
|
If the dataset misses feature columns. |
PredictionError
|
If predicting with the given dataset failed. |