Quality evaluations

Measuring model accuracy with quality evaluations

Quality evaluations monitor how well your model predicts accurate outcomes. It identifies when model quality declines, so you can retrain your model appropriately. To evaluate the model, you provide feedback data, which is labeled data where the outcome is known. Quality evaluations use a set of standard data science metrics to evaluate how well the model predicts outcome that match the actual outcomes in the labeled data set.

You can set the acceptable quality thresholds for the metrics used to evaluate your model. You can also set the sample size, which is the number of rows of feedback data, to consider for the evaluation.

Before you begin: Providing the feedback data

The feedback data is like providing an answer sheet with actual observed outcomes. The monitor can run the model as if the answers are not known, then compare the predicted outcomes to the actual outcomes and provide accuracy scores based on quality metrics.

To provide the feedback data for machine learning models, you must open the Endpoints page and do one of the following:

• Click Upload feedback data and upload a file with labeled data.
• Click the Endpoints tab and specify an endpoint that connects to the feedback data source.

For details, see Managing feedback data.

Setting Quality thresholds

After your feedback data is available for the evaluation, configure the monitor settings. You set thresholds for acceptable performance for the model as compared to the known outcomes.

To set the threshold values, from the Quality tab, click the Edit icon to enter values for Quality threshold box, then edit the values for sample size.

Supported quality metrics

When you enable quality evaluations, you can generate metrics that help you determine how well your model predicts outcomes.

You can view the results of your quality evaluations on the evaluation summary page. To view results, you can select a model deployment tile and click the arrow in the Quality evaluation section to display a summary of quality metrics from your last evaluation. For more information, see Reviewing quality results.

Area under ROC

• Description: Area under recall and false positive rate curve to calculate sensitivity against the fallout rate
• Default thresholds: Lower limit = 80%
• Problem type: Binary classification
• Chart values: Last value in the timeframe
• Metrics details available: Confusion matrix

Area under PR

• Description: Area under precision and recall curve
• Default thresholds: Lower limit = 80%
• Problem type: Binary classification
• Chart values: Last value in the timeframe
• Metrics details available: Confusion matrix
• Do the math:

Area under Precision Recall gives the total for both Precision + Recall.

       n
AveP = ∑ P(k)∆r(k)
      k=1

Precision (P) is defined as the number of true positives (Tp) over the number of true positives plus the number of false positives (Fp).

               number of true positives
Precision =   ______________________________________________________

              (number of true positives + number of false positives)

Recall (R) is defined as the number of true positives (Tp) over the number of true positives plus the number of false negatives (Fn).

            number of true positives
Recall =   ______________________________________________________

           (number of true positives + number of false negatives)

Accuracy

• Description: The proportion of correct predictions
• Default thresholds: Lower limit = 80%
• Problem types: Binary classification and multiclass classification
• Chart values: Last value in the timeframe
• Metrics details available: Confusion matrix
• Understanding accuracy:
  Accuracy can mean different things depending on the type of algorithm;

  • Multi-class classification: Accuracy measures the number of times any class was predicted correctly, normalized by the number of data points. For more details, see Multi-class classification in the Apache Spark documentation.

  • Binary classification: For a binary classification algorithm, accuracy is measured as the area under an ROC curve. See Binary classification in the Apache Spark documentation for more details.

  • Regression: Regression algorithms are measured by using the Coefficient of Determination, or R2. For more details, see Regression model evaluation in the Apache Spark documentation.

True positive rate

• Description: Proportion of correct predictions in predictions of positive class
• Default thresholds: lower limit = 80%
• Problem type: Binary classification
• Chart values: Last value in the timeframe
• Metrics details available: Confusion matrix
• Do the math:

The True positive rate is calculated by the following formula:

                  number of true positives
TPR =  _________________________________________________________

        (number of true positives + number of false negatives)

False positive rate

• Description: Proportion of incorrect predictions in positive class
• Default thresholds: Lower limit = 80%
• Problem type: Binary classification
• Chart values: Last value in the timeframe
• Metrics details available: Confusion matrix
• Do the math:

The false positive rate is quotient of the total number of false positives that is divided by the sum of false positives and true negatives.

                        number of false positives
False positive rate =  ______________________________________________________

                       (number of false positives + number of true negatives)

Recall

• Description: Proportion of correct predictions in positive class
• Default thresholds: Lower limit = 80%
• Problem type: Binary classification
• Chart values: Last value in the timeframe
• Metrics details available: Confusion matrix
• Do th math:

Recall (R) is defined as the number of true positives (Tp) over the number of true positives plus the number of false negatives (Fn).

                       number of true positives
Recall =   ______________________________________________________

           (number of true positives + number of false negatives)

Precision

• Description: Proportion of correct predictions in predictions of positive class
• Default thresholds: Lower limit = 80%
• Problem type: Binary classification
• Chart values: Last value in the timeframe
• Metrics details available: Confusion matrix
• Do the math:

Precision (P) is defined as the number of true positives (Tp) over the number of true positives plus the number of false positives (Fp).

                           number of true positives
Precision =  __________________________________________________________

             (number of true positives + the number of false positives)

F1-Measure

• Description: Harmonic mean of precision and recall
• Default thresholds: Lower limit = 80%
• Problem type: Binary classification
• Chart values: Last value in the timeframe
• Metrics details available: Confusion matrix
• Do the math:

The F1-measure is the weighted harmonic average or mean of precision and recall.

          (precision * recall)
F1 = 2 *  ____________________

          (precision + recall)

Gini coefficient

• Description: Gini coefficient measures how well models distinguish between two classes. It is calculated as twice the area between the ROC curve and the diagonal line of the graph plot. If the gini coefficient value is 0, the model shows no discrimination ability and a value of 1 indicates perfect discrimination.
• Default thresholds:
  • Lower limit = 80%
• Problem type: Binary classification
• Chart values: Last value in the timeframe
• Metrics details available: Confusion matrix
• Do the math:

The gini coefficient metric is calculated with the following formula:

Gini = 2 * Area under ROC - 1

Logarithmic loss

• Description: Mean of logarithms target class probabilities (confidence). It is also known as Expected log-likelihood.
• Default thresholds: Lower limit = 80%
• Problem type: Binary classification and multiclass classification
• Chart values: Last value in the timeframe
• Metrics details available: None
• Do the math:

For a binary model, Logarithmic loss is calculated by using the following formula:

-(y log(p) + (1-y)log(1-p))

Where p = true label and y = predicted probability

For a multi-class model, Logarithmic loss is calculated by using the following formula:

  M
-SUM Yo,c log(Po,c)
 c=1 

Where M > 2, p = true label, and y = predicted probability

Proportion explained variance

• Description: Proportion explained variance is the ratio of explained variance and target variance. Explained variance is the difference between target variance and variance of prediction error.
• Default thresholds: Lower limit = 80%
• Problem type: Regression
• Chart values: Last value in the timeframe
• Metrics details available: None
• Do the math:

The Proportion explained variance is calculated by averaging the numbers, then for each number, subtract the mean, and square the results. Then, work out the squares.

                                  sum of squares between groups 
Proportion explained variance =  ________________________________

                                      sum of squares total

Mean-absolute error

• Description: Mean of absolute difference between model prediction and target value
• Default thresholds: Upper limit = 80%
• Problem type: Regression
• Chart values: Last value in the timeframe
• Metrics details available: None
• Do the math:

The Mean absolute error is calculated by adding up all the absolute errors and dividing them by the number of errors.

                         SUM  | Yi - Xi | 
Mean absolute errors =  ____________________

                          number of errors

Mean-squared error

• Description: Mean of squared difference between model prediction and target value
• Default thresholds: Upper limit = 80%
• Problem type: Regression
• Chart values: Last value in the timeframe
• Metrics details available: None
• Do the math:

The Mean squared error in its simplest form is represented by the following formula.

                         SUM  (Yi - ^Yi) * (Yi - ^Yi)
Mean squared errors  =  ____________________________

                             number of errors

R-squared

• Description: Ratio of difference between target variance and variance for prediction error to target variance
• Default thresholds: Lower limit = 80%
• Problem type: Regression
• Chart values: Last value in the timeframe
• Metrics details available: None
• Do the math:

The R-squared metric is defined in the following formula.

                  explained variation
R-squared =       _____________________

                    total variation

Root of mean squared error

• Description: Square root of mean of squared difference between model prediction and target value
• Default thresholds: Upper limit = 80%
• Problem type: Regression
• Chart values: Last value in the timeframe
• Metrics details available: None
• Do the math:

The root of the mean-squared error is equal to the square root of the mean of (forecasts minus observed values) squared.

          ___________________________________________________________
RMSE  =  √(forecasts - observed values)*(forecasts - observed values)

Weighted True Positive Rate

• Description: Weighted mean of class TPR with weights equal to class probability
• Default thresholds: Lower limit = 80%
• Problem type: Multiclass classification
• Chart values: Last value in the timeframe
• Metrics details available: Confusion matrix
• Do the math:

The True positive rate is calculated by the following formula:

                  number of true positives
TPR =  _________________________________________________________

        number of true positives + number of false negatives

Weighted False Positive Rate

• Description: Proportion of incorrect predictions in positive class
• Default thresholds: Lower limit = 80%
• Problem type: Multiclass classification
• Chart values: Last value in the timeframe
• Metrics details available: Confusion matrix
• Do the math:

The Weighted False Positive Rate is the application of the FPR with weighted data.

                   number of false positives
FPR =  ______________________________________________________

       (number of false positives + number of true negatives)

Weighted recall

• Description: Weighted mean of recall with weights equal to class probability
• Default thresholds: Lower limit = 80%
• Problem type: Multiclass classification
• Chart values: Last value in the timeframe
• Metrics details available: Confusion matrix
• Do the math:

Weighted recall (wR) is defined as the number of true positives (Tp) over the number of true positives plus the number of false negatives (Fn) used with weighted data.

                          number of true positives
Recall =   ______________________________________________________

           number of true positives + number of false negatives

Weighted precision

• Description: Weighted mean of precision with weights equal to class probability
• Default thresholds: Lower limit = 80%
• Problem type: Multiclass classification
• Chart values: Last value in the timeframe
• Metrics details available: Confusion matrix
• Do the math:

Precision (P) is defined as the number of true positives (Tp) over the number of true positives plus the number of false positives (Fp).

                            number of true positives
Precision =  ________________________________________________________

             number of true positives + the number of false positives

Weighted F1-Measure

• Description: Weighted mean of F1-measure with weights equal to class probability
• Default thresholds: Lower limit = 80%
• Problem type: Multiclass classification
• Chart values: Last value in the timeframe
• Metrics details available: Confusion matrix
• Do the math:

The Weighted F1-Measure is the result of using weighted data.

           precision * recall
F1 = 2 *  ____________________

           precision + recall

Configuring quality evaluations with historical data

You can also configure quality evaluations to generate metrics with historical scored feedback data from previous time windows. To configure evaluations with historical scored feedback data, you can use the Python SDK to specify parameters to compute metrics in a single time window with start and end dates:

parameters = {
    "start_date": "2024-08-05T11:00:18.0000Z",
    "end_date": "2024-08-05T14:00:18.0000Z"
}
run_details = wos_client.monitor_instances.run(monitor_instance_id=quality_monitor_instance_id, parameters = run_parameters, background_mode=False).result

Learn more

Reviewing quality results for machine learning models

Parent topic: Configuring model evaluations