@Generated(value="com.amazonaws:aws-java-sdk-code-generator") public class AutoMLJobObjective extends Object implements Serializable, Cloneable, StructuredPojo
Specifies a metric to minimize or maximize as the objective of a job.
| Constructor and Description |
|---|
AutoMLJobObjective() |
| Modifier and Type | Method and Description |
|---|---|
AutoMLJobObjective |
clone() |
boolean |
equals(Object obj) |
String |
getMetricName()
The name of the objective metric used to measure the predictive quality of a machine learning system.
|
int |
hashCode() |
void |
marshall(ProtocolMarshaller protocolMarshaller)
Marshalls this structured data using the given
ProtocolMarshaller. |
void |
setMetricName(String metricName)
The name of the objective metric used to measure the predictive quality of a machine learning system.
|
String |
toString()
Returns a string representation of this object.
|
AutoMLJobObjective |
withMetricName(AutoMLMetricEnum metricName)
The name of the objective metric used to measure the predictive quality of a machine learning system.
|
AutoMLJobObjective |
withMetricName(String metricName)
The name of the objective metric used to measure the predictive quality of a machine learning system.
|
public void setMetricName(String metricName)
The name of the objective metric used to measure the predictive quality of a machine learning system. This metric is optimized during training to provide the best estimate for model parameter values from data.
Here are the options:
MSE: The mean squared error (MSE) is the average of the squared differences between the predicted
and actual values. It is used for regression. MSE values are always positive, the better a model is at predicting
the actual values the smaller the MSE value. When the data contains outliers, they tend to dominate the MSE which
might cause subpar prediction performance.
Accuracy: The ratio of the number correctly classified items to the total number (correctly and
incorrectly) classified. It is used for binary and multiclass classification. Measures how close the predicted
class values are to the actual values. Accuracy values vary between zero and one, one being perfect accuracy and
zero perfect inaccuracy.
F1: The F1 score is the harmonic mean of the precision and recall. It is used for binary
classification into classes traditionally referred to as positive and negative. Predictions are said to be true
when they match their actual (correct) class; false when they do not. Precision is the ratio of the true positive
predictions to all positive predictions (including the false positives) in a data set and measures the quality of
the prediction when it predicts the positive class. Recall (or sensitivity) is the ratio of the true positive
predictions to all actual positive instances and measures how completely a model predicts the actual class
members in a data set. The standard F1 score weighs precision and recall equally. But which metric is paramount
typically depends on specific aspects of a problem. F1 scores vary between zero and one, one being the best
possible performance and zero the worst.
AUC: The area under the curve (AUC) metric is used to compare and evaluate binary classification by
algorithms such as logistic regression that return probabilities. A threshold is needed to map the probabilities
into classifications. The relevant curve is the receiver operating characteristic curve that plots the true
positive rate (TPR) of predictions (or recall) against the false positive rate (FPR) as a function of the
threshold value, above which a prediction is considered positive. Increasing the threshold results in fewer false
positives but more false negatives. AUC is the area under this receiver operating characteristic curve and so
provides an aggregated measure of the model performance across all possible classification thresholds. The AUC
score can also be interpreted as the probability that a randomly selected positive data point is more likely to
be predicted positive than a randomly selected negative example. AUC scores vary between zero and one, one being
perfect accuracy and one half not better than a random classifier. Values less that one half predict worse than a
random predictor and such consistently bad predictors can be inverted to obtain better than random predictors.
F1macro: The F1macro score applies F1 scoring to multiclass classification. In this context, you
have multiple classes to predict. You just calculate the precision and recall for each class as you did for the
positive class in binary classification. Then used these values to calculate the F1 score for each class and
average them to obtain the F1macro score. F1macro scores vary between zero and one, one being the best possible
performance and zero the worst.
If you do not specify a metric explicitly, the default behavior is to automatically use:
MSE: for regression.
F1: for binary classification
Accuracy: for multiclass classification.
metricName - The name of the objective metric used to measure the predictive quality of a machine learning system. This
metric is optimized during training to provide the best estimate for model parameter values from data.
Here are the options:
MSE: The mean squared error (MSE) is the average of the squared differences between the
predicted and actual values. It is used for regression. MSE values are always positive, the better a model
is at predicting the actual values the smaller the MSE value. When the data contains outliers, they tend
to dominate the MSE which might cause subpar prediction performance.
Accuracy: The ratio of the number correctly classified items to the total number (correctly
and incorrectly) classified. It is used for binary and multiclass classification. Measures how close the
predicted class values are to the actual values. Accuracy values vary between zero and one, one being
perfect accuracy and zero perfect inaccuracy.
F1: The F1 score is the harmonic mean of the precision and recall. It is used for binary
classification into classes traditionally referred to as positive and negative. Predictions are said to be
true when they match their actual (correct) class; false when they do not. Precision is the ratio of the
true positive predictions to all positive predictions (including the false positives) in a data set and
measures the quality of the prediction when it predicts the positive class. Recall (or sensitivity) is the
ratio of the true positive predictions to all actual positive instances and measures how completely a
model predicts the actual class members in a data set. The standard F1 score weighs precision and recall
equally. But which metric is paramount typically depends on specific aspects of a problem. F1 scores vary
between zero and one, one being the best possible performance and zero the worst.
AUC: The area under the curve (AUC) metric is used to compare and evaluate binary
classification by algorithms such as logistic regression that return probabilities. A threshold is needed
to map the probabilities into classifications. The relevant curve is the receiver operating characteristic
curve that plots the true positive rate (TPR) of predictions (or recall) against the false positive rate
(FPR) as a function of the threshold value, above which a prediction is considered positive. Increasing
the threshold results in fewer false positives but more false negatives. AUC is the area under this
receiver operating characteristic curve and so provides an aggregated measure of the model performance
across all possible classification thresholds. The AUC score can also be interpreted as the probability
that a randomly selected positive data point is more likely to be predicted positive than a randomly
selected negative example. AUC scores vary between zero and one, one being perfect accuracy and one half
not better than a random classifier. Values less that one half predict worse than a random predictor and
such consistently bad predictors can be inverted to obtain better than random predictors.
F1macro: The F1macro score applies F1 scoring to multiclass classification. In this context,
you have multiple classes to predict. You just calculate the precision and recall for each class as you
did for the positive class in binary classification. Then used these values to calculate the F1 score for
each class and average them to obtain the F1macro score. F1macro scores vary between zero and one, one
being the best possible performance and zero the worst.
If you do not specify a metric explicitly, the default behavior is to automatically use:
MSE: for regression.
F1: for binary classification
Accuracy: for multiclass classification.
AutoMLMetricEnumpublic String getMetricName()
The name of the objective metric used to measure the predictive quality of a machine learning system. This metric is optimized during training to provide the best estimate for model parameter values from data.
Here are the options:
MSE: The mean squared error (MSE) is the average of the squared differences between the predicted
and actual values. It is used for regression. MSE values are always positive, the better a model is at predicting
the actual values the smaller the MSE value. When the data contains outliers, they tend to dominate the MSE which
might cause subpar prediction performance.
Accuracy: The ratio of the number correctly classified items to the total number (correctly and
incorrectly) classified. It is used for binary and multiclass classification. Measures how close the predicted
class values are to the actual values. Accuracy values vary between zero and one, one being perfect accuracy and
zero perfect inaccuracy.
F1: The F1 score is the harmonic mean of the precision and recall. It is used for binary
classification into classes traditionally referred to as positive and negative. Predictions are said to be true
when they match their actual (correct) class; false when they do not. Precision is the ratio of the true positive
predictions to all positive predictions (including the false positives) in a data set and measures the quality of
the prediction when it predicts the positive class. Recall (or sensitivity) is the ratio of the true positive
predictions to all actual positive instances and measures how completely a model predicts the actual class
members in a data set. The standard F1 score weighs precision and recall equally. But which metric is paramount
typically depends on specific aspects of a problem. F1 scores vary between zero and one, one being the best
possible performance and zero the worst.
AUC: The area under the curve (AUC) metric is used to compare and evaluate binary classification by
algorithms such as logistic regression that return probabilities. A threshold is needed to map the probabilities
into classifications. The relevant curve is the receiver operating characteristic curve that plots the true
positive rate (TPR) of predictions (or recall) against the false positive rate (FPR) as a function of the
threshold value, above which a prediction is considered positive. Increasing the threshold results in fewer false
positives but more false negatives. AUC is the area under this receiver operating characteristic curve and so
provides an aggregated measure of the model performance across all possible classification thresholds. The AUC
score can also be interpreted as the probability that a randomly selected positive data point is more likely to
be predicted positive than a randomly selected negative example. AUC scores vary between zero and one, one being
perfect accuracy and one half not better than a random classifier. Values less that one half predict worse than a
random predictor and such consistently bad predictors can be inverted to obtain better than random predictors.
F1macro: The F1macro score applies F1 scoring to multiclass classification. In this context, you
have multiple classes to predict. You just calculate the precision and recall for each class as you did for the
positive class in binary classification. Then used these values to calculate the F1 score for each class and
average them to obtain the F1macro score. F1macro scores vary between zero and one, one being the best possible
performance and zero the worst.
If you do not specify a metric explicitly, the default behavior is to automatically use:
MSE: for regression.
F1: for binary classification
Accuracy: for multiclass classification.
Here are the options:
MSE: The mean squared error (MSE) is the average of the squared differences between the
predicted and actual values. It is used for regression. MSE values are always positive, the better a
model is at predicting the actual values the smaller the MSE value. When the data contains outliers, they
tend to dominate the MSE which might cause subpar prediction performance.
Accuracy: The ratio of the number correctly classified items to the total number (correctly
and incorrectly) classified. It is used for binary and multiclass classification. Measures how close the
predicted class values are to the actual values. Accuracy values vary between zero and one, one being
perfect accuracy and zero perfect inaccuracy.
F1: The F1 score is the harmonic mean of the precision and recall. It is used for binary
classification into classes traditionally referred to as positive and negative. Predictions are said to
be true when they match their actual (correct) class; false when they do not. Precision is the ratio of
the true positive predictions to all positive predictions (including the false positives) in a data set
and measures the quality of the prediction when it predicts the positive class. Recall (or sensitivity)
is the ratio of the true positive predictions to all actual positive instances and measures how
completely a model predicts the actual class members in a data set. The standard F1 score weighs
precision and recall equally. But which metric is paramount typically depends on specific aspects of a
problem. F1 scores vary between zero and one, one being the best possible performance and zero the worst.
AUC: The area under the curve (AUC) metric is used to compare and evaluate binary
classification by algorithms such as logistic regression that return probabilities. A threshold is needed
to map the probabilities into classifications. The relevant curve is the receiver operating
characteristic curve that plots the true positive rate (TPR) of predictions (or recall) against the false
positive rate (FPR) as a function of the threshold value, above which a prediction is considered
positive. Increasing the threshold results in fewer false positives but more false negatives. AUC is the
area under this receiver operating characteristic curve and so provides an aggregated measure of the
model performance across all possible classification thresholds. The AUC score can also be interpreted as
the probability that a randomly selected positive data point is more likely to be predicted positive than
a randomly selected negative example. AUC scores vary between zero and one, one being perfect accuracy
and one half not better than a random classifier. Values less that one half predict worse than a random
predictor and such consistently bad predictors can be inverted to obtain better than random predictors.
F1macro: The F1macro score applies F1 scoring to multiclass classification. In this context,
you have multiple classes to predict. You just calculate the precision and recall for each class as you
did for the positive class in binary classification. Then used these values to calculate the F1 score for
each class and average them to obtain the F1macro score. F1macro scores vary between zero and one, one
being the best possible performance and zero the worst.
If you do not specify a metric explicitly, the default behavior is to automatically use:
MSE: for regression.
F1: for binary classification
Accuracy: for multiclass classification.
AutoMLMetricEnumpublic AutoMLJobObjective withMetricName(String metricName)
The name of the objective metric used to measure the predictive quality of a machine learning system. This metric is optimized during training to provide the best estimate for model parameter values from data.
Here are the options:
MSE: The mean squared error (MSE) is the average of the squared differences between the predicted
and actual values. It is used for regression. MSE values are always positive, the better a model is at predicting
the actual values the smaller the MSE value. When the data contains outliers, they tend to dominate the MSE which
might cause subpar prediction performance.
Accuracy: The ratio of the number correctly classified items to the total number (correctly and
incorrectly) classified. It is used for binary and multiclass classification. Measures how close the predicted
class values are to the actual values. Accuracy values vary between zero and one, one being perfect accuracy and
zero perfect inaccuracy.
F1: The F1 score is the harmonic mean of the precision and recall. It is used for binary
classification into classes traditionally referred to as positive and negative. Predictions are said to be true
when they match their actual (correct) class; false when they do not. Precision is the ratio of the true positive
predictions to all positive predictions (including the false positives) in a data set and measures the quality of
the prediction when it predicts the positive class. Recall (or sensitivity) is the ratio of the true positive
predictions to all actual positive instances and measures how completely a model predicts the actual class
members in a data set. The standard F1 score weighs precision and recall equally. But which metric is paramount
typically depends on specific aspects of a problem. F1 scores vary between zero and one, one being the best
possible performance and zero the worst.
AUC: The area under the curve (AUC) metric is used to compare and evaluate binary classification by
algorithms such as logistic regression that return probabilities. A threshold is needed to map the probabilities
into classifications. The relevant curve is the receiver operating characteristic curve that plots the true
positive rate (TPR) of predictions (or recall) against the false positive rate (FPR) as a function of the
threshold value, above which a prediction is considered positive. Increasing the threshold results in fewer false
positives but more false negatives. AUC is the area under this receiver operating characteristic curve and so
provides an aggregated measure of the model performance across all possible classification thresholds. The AUC
score can also be interpreted as the probability that a randomly selected positive data point is more likely to
be predicted positive than a randomly selected negative example. AUC scores vary between zero and one, one being
perfect accuracy and one half not better than a random classifier. Values less that one half predict worse than a
random predictor and such consistently bad predictors can be inverted to obtain better than random predictors.
F1macro: The F1macro score applies F1 scoring to multiclass classification. In this context, you
have multiple classes to predict. You just calculate the precision and recall for each class as you did for the
positive class in binary classification. Then used these values to calculate the F1 score for each class and
average them to obtain the F1macro score. F1macro scores vary between zero and one, one being the best possible
performance and zero the worst.
If you do not specify a metric explicitly, the default behavior is to automatically use:
MSE: for regression.
F1: for binary classification
Accuracy: for multiclass classification.
metricName - The name of the objective metric used to measure the predictive quality of a machine learning system. This
metric is optimized during training to provide the best estimate for model parameter values from data.
Here are the options:
MSE: The mean squared error (MSE) is the average of the squared differences between the
predicted and actual values. It is used for regression. MSE values are always positive, the better a model
is at predicting the actual values the smaller the MSE value. When the data contains outliers, they tend
to dominate the MSE which might cause subpar prediction performance.
Accuracy: The ratio of the number correctly classified items to the total number (correctly
and incorrectly) classified. It is used for binary and multiclass classification. Measures how close the
predicted class values are to the actual values. Accuracy values vary between zero and one, one being
perfect accuracy and zero perfect inaccuracy.
F1: The F1 score is the harmonic mean of the precision and recall. It is used for binary
classification into classes traditionally referred to as positive and negative. Predictions are said to be
true when they match their actual (correct) class; false when they do not. Precision is the ratio of the
true positive predictions to all positive predictions (including the false positives) in a data set and
measures the quality of the prediction when it predicts the positive class. Recall (or sensitivity) is the
ratio of the true positive predictions to all actual positive instances and measures how completely a
model predicts the actual class members in a data set. The standard F1 score weighs precision and recall
equally. But which metric is paramount typically depends on specific aspects of a problem. F1 scores vary
between zero and one, one being the best possible performance and zero the worst.
AUC: The area under the curve (AUC) metric is used to compare and evaluate binary
classification by algorithms such as logistic regression that return probabilities. A threshold is needed
to map the probabilities into classifications. The relevant curve is the receiver operating characteristic
curve that plots the true positive rate (TPR) of predictions (or recall) against the false positive rate
(FPR) as a function of the threshold value, above which a prediction is considered positive. Increasing
the threshold results in fewer false positives but more false negatives. AUC is the area under this
receiver operating characteristic curve and so provides an aggregated measure of the model performance
across all possible classification thresholds. The AUC score can also be interpreted as the probability
that a randomly selected positive data point is more likely to be predicted positive than a randomly
selected negative example. AUC scores vary between zero and one, one being perfect accuracy and one half
not better than a random classifier. Values less that one half predict worse than a random predictor and
such consistently bad predictors can be inverted to obtain better than random predictors.
F1macro: The F1macro score applies F1 scoring to multiclass classification. In this context,
you have multiple classes to predict. You just calculate the precision and recall for each class as you
did for the positive class in binary classification. Then used these values to calculate the F1 score for
each class and average them to obtain the F1macro score. F1macro scores vary between zero and one, one
being the best possible performance and zero the worst.
If you do not specify a metric explicitly, the default behavior is to automatically use:
MSE: for regression.
F1: for binary classification
Accuracy: for multiclass classification.
AutoMLMetricEnumpublic AutoMLJobObjective withMetricName(AutoMLMetricEnum metricName)
The name of the objective metric used to measure the predictive quality of a machine learning system. This metric is optimized during training to provide the best estimate for model parameter values from data.
Here are the options:
MSE: The mean squared error (MSE) is the average of the squared differences between the predicted
and actual values. It is used for regression. MSE values are always positive, the better a model is at predicting
the actual values the smaller the MSE value. When the data contains outliers, they tend to dominate the MSE which
might cause subpar prediction performance.
Accuracy: The ratio of the number correctly classified items to the total number (correctly and
incorrectly) classified. It is used for binary and multiclass classification. Measures how close the predicted
class values are to the actual values. Accuracy values vary between zero and one, one being perfect accuracy and
zero perfect inaccuracy.
F1: The F1 score is the harmonic mean of the precision and recall. It is used for binary
classification into classes traditionally referred to as positive and negative. Predictions are said to be true
when they match their actual (correct) class; false when they do not. Precision is the ratio of the true positive
predictions to all positive predictions (including the false positives) in a data set and measures the quality of
the prediction when it predicts the positive class. Recall (or sensitivity) is the ratio of the true positive
predictions to all actual positive instances and measures how completely a model predicts the actual class
members in a data set. The standard F1 score weighs precision and recall equally. But which metric is paramount
typically depends on specific aspects of a problem. F1 scores vary between zero and one, one being the best
possible performance and zero the worst.
AUC: The area under the curve (AUC) metric is used to compare and evaluate binary classification by
algorithms such as logistic regression that return probabilities. A threshold is needed to map the probabilities
into classifications. The relevant curve is the receiver operating characteristic curve that plots the true
positive rate (TPR) of predictions (or recall) against the false positive rate (FPR) as a function of the
threshold value, above which a prediction is considered positive. Increasing the threshold results in fewer false
positives but more false negatives. AUC is the area under this receiver operating characteristic curve and so
provides an aggregated measure of the model performance across all possible classification thresholds. The AUC
score can also be interpreted as the probability that a randomly selected positive data point is more likely to
be predicted positive than a randomly selected negative example. AUC scores vary between zero and one, one being
perfect accuracy and one half not better than a random classifier. Values less that one half predict worse than a
random predictor and such consistently bad predictors can be inverted to obtain better than random predictors.
F1macro: The F1macro score applies F1 scoring to multiclass classification. In this context, you
have multiple classes to predict. You just calculate the precision and recall for each class as you did for the
positive class in binary classification. Then used these values to calculate the F1 score for each class and
average them to obtain the F1macro score. F1macro scores vary between zero and one, one being the best possible
performance and zero the worst.
If you do not specify a metric explicitly, the default behavior is to automatically use:
MSE: for regression.
F1: for binary classification
Accuracy: for multiclass classification.
metricName - The name of the objective metric used to measure the predictive quality of a machine learning system. This
metric is optimized during training to provide the best estimate for model parameter values from data.
Here are the options:
MSE: The mean squared error (MSE) is the average of the squared differences between the
predicted and actual values. It is used for regression. MSE values are always positive, the better a model
is at predicting the actual values the smaller the MSE value. When the data contains outliers, they tend
to dominate the MSE which might cause subpar prediction performance.
Accuracy: The ratio of the number correctly classified items to the total number (correctly
and incorrectly) classified. It is used for binary and multiclass classification. Measures how close the
predicted class values are to the actual values. Accuracy values vary between zero and one, one being
perfect accuracy and zero perfect inaccuracy.
F1: The F1 score is the harmonic mean of the precision and recall. It is used for binary
classification into classes traditionally referred to as positive and negative. Predictions are said to be
true when they match their actual (correct) class; false when they do not. Precision is the ratio of the
true positive predictions to all positive predictions (including the false positives) in a data set and
measures the quality of the prediction when it predicts the positive class. Recall (or sensitivity) is the
ratio of the true positive predictions to all actual positive instances and measures how completely a
model predicts the actual class members in a data set. The standard F1 score weighs precision and recall
equally. But which metric is paramount typically depends on specific aspects of a problem. F1 scores vary
between zero and one, one being the best possible performance and zero the worst.
AUC: The area under the curve (AUC) metric is used to compare and evaluate binary
classification by algorithms such as logistic regression that return probabilities. A threshold is needed
to map the probabilities into classifications. The relevant curve is the receiver operating characteristic
curve that plots the true positive rate (TPR) of predictions (or recall) against the false positive rate
(FPR) as a function of the threshold value, above which a prediction is considered positive. Increasing
the threshold results in fewer false positives but more false negatives. AUC is the area under this
receiver operating characteristic curve and so provides an aggregated measure of the model performance
across all possible classification thresholds. The AUC score can also be interpreted as the probability
that a randomly selected positive data point is more likely to be predicted positive than a randomly
selected negative example. AUC scores vary between zero and one, one being perfect accuracy and one half
not better than a random classifier. Values less that one half predict worse than a random predictor and
such consistently bad predictors can be inverted to obtain better than random predictors.
F1macro: The F1macro score applies F1 scoring to multiclass classification. In this context,
you have multiple classes to predict. You just calculate the precision and recall for each class as you
did for the positive class in binary classification. Then used these values to calculate the F1 score for
each class and average them to obtain the F1macro score. F1macro scores vary between zero and one, one
being the best possible performance and zero the worst.
If you do not specify a metric explicitly, the default behavior is to automatically use:
MSE: for regression.
F1: for binary classification
Accuracy: for multiclass classification.
AutoMLMetricEnumpublic String toString()
toString in class ObjectObject.toString()public AutoMLJobObjective clone()
public void marshall(ProtocolMarshaller protocolMarshaller)
StructuredPojoProtocolMarshaller.marshall in interface StructuredPojoprotocolMarshaller - Implementation of ProtocolMarshaller used to marshall this object's data.