class Aws::SageMaker::Types::AutoMLJobObjective

Specifies a metric to minimize or maximize as the objective of a job.

@note When making an API call, you may pass AutoMLJobObjective

data as a hash:

    {
      metric_name: "Accuracy", # required, accepts Accuracy, MSE, F1, F1macro, AUC
    }

@!attribute [rw] metric_name

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 is.
  When the data contains outliers, they tend to dominate the MSE,
  which might cause subpar prediction performance.

* `Accuracy`\: The ratio of the number of correctly classified items
  to the total number of (correctly and incorrectly) classified
  items. It is used for binary and multiclass classification. It
  measures how close the predicted class values are to the actual
  values. Accuracy values vary between zero and one: one indicates
  perfect accuracy and zero indicates 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
  and 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 indicates 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: a score of one indicates perfect
  accuracy and a score of one half indicates that the prediction is
  not better than a random classifier. Values under one half predict
  less accurately than a random predictor. But such consistently bad
  predictors can simply 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, use 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 indicates 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.
@return [String]

@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/AutoMLJobObjective AWS API Documentation

Constants

SENSITIVE