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Task Metrics#

Kolena supports automatic calculation of a number of metrics commonly used in machine learning tasks.

Regression#

For regression problems, Kolena web application currently supports mean_absolute_error, mean_squared_error, root_mean_squared_error, r^2, pearson_correlation and spearman_correlation

Follow the steps below to setup Regression metrics:

Defining Metrics Defining Metrics

Task Metrics: Regression

Binary Classification#

For binary classification problems, Kolena web application currently supports accuracy,precision, recall and f1_score.

Follow the steps below to setup Binary Classification metrics:

Defining Metrics Defining Metrics

Task Metrics: Binary classification

Multiclass Classification#

For multiclass classification problems, Kolena web application currently supports accuracy,precision, recall and f1_score.

You are able to apply macro, micro and weighted averaging methods to above metrics.

Defining Metrics Defining Metrics

Task Metrics: Multiclass classification

Object detection#

For object detection problems, Kolena web application currently supports average precision, precision, recall and f1_score.

Required fields

Kolena attempts to automatically detect fields for true positive, false positive and false negative counts. For more information on how to prepare your object detection data, please visit Formatting results for Object Detection

Object detection Object detection

Task Metrics: Object detection

Custom Metrics#

Kolena provides out-of-the-box aggregation options for your datapoint level evaluations that correspond with your desired metrics. For numeric evaluations you are able to select from count, mean, median, min, max, stddev and sum aggregations options. For categorical evaluations (class lable, boolean, etc) rate and count aggregation options are available.

The Kolena web application currently supports precision, recall, f1_score, accuracy, false_positive_rate, and true_negative_rate.

To leverage these, add the following columns to your model result file: count_TP, count_FP, count_FN, count_TN.

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Custom metrics