Icon

02. Evaluation Metrics

Binary Classification Metrics Census DatasetExtraction was done by Barry Becker from the 1994Census database. A set of reasonably clean recordswas extracted using the following conditions:((AAGE>16) && (AGI>100) && (AFNLWGT>1)&&(HRSWK>0)) 48842 rows.Prediction task is to determine whether a personmakes over 50K a year. https://archive.ics.uci.edu/ml/datasets/Adult MultiClass Classification Metrics Iris DatasetThe Iris flower data set or Fisher's Iris data set is amultivariate data set introduced by the Britishstatistician and biologist Ronald Fisher in his 1936paper The use of multiple measurements intaxonomic problems as an example of lineardiscriminant analysis.The data set consists of 50 samples from each ofthree species of Iris (Iris setosa, Iris virginica and Irisversicolor). Four features were measured from eachsample: the length and the width of the sepals andpetals, in centimetres. Based on the combination ofthese four features, Fisher developed a lineardiscriminant model to distinguish the species fromeach other. Wine DatasetTwo datasets are included, related to red and whitevinho verde wine samples, from the north of Portugal.The goal is to model wine quality based onphysicochemical tests (see [Cortez et al., 2009).The inputs include objective tests (e.g. PH values)and the output is based on sensory data (median ofat least 3 evaluations made by wine experts). Eachexpert graded the wine quality between 0 (very bad)and 10 (very excellent).https://archive.ics.uci.edu/ml/datasets/Wine+Quality Regression Metrics train aclassificationmodelevaluate a classificationmodelLoad Irisevaluate metricsLoad Census Datatrain aclassificationmodelevaluate a classificationmodelevaluate metricsLoad Wine Datatrain aregressionmodelevaluate a regressionmodelevaluate metricsNode 14 Training Evaluate ARFF Reader Scorer CSV Reader Training Evaluate Scorer CSV Reader Training Evaluate Numeric Scorer(deprecated) CSV Writer Binary Classification Metrics Census DatasetExtraction was done by Barry Becker from the 1994Census database. A set of reasonably clean recordswas extracted using the following conditions:((AAGE>16) && (AGI>100) && (AFNLWGT>1)&&(HRSWK>0)) 48842 rows.Prediction task is to determine whether a personmakes over 50K a year. https://archive.ics.uci.edu/ml/datasets/Adult MultiClass Classification Metrics Iris DatasetThe Iris flower data set or Fisher's Iris data set is amultivariate data set introduced by the Britishstatistician and biologist Ronald Fisher in his 1936paper The use of multiple measurements intaxonomic problems as an example of lineardiscriminant analysis.The data set consists of 50 samples from each ofthree species of Iris (Iris setosa, Iris virginica and Irisversicolor). Four features were measured from eachsample: the length and the width of the sepals andpetals, in centimetres. Based on the combination ofthese four features, Fisher developed a lineardiscriminant model to distinguish the species fromeach other. Wine DatasetTwo datasets are included, related to red and whitevinho verde wine samples, from the north of Portugal.The goal is to model wine quality based onphysicochemical tests (see [Cortez et al., 2009).The inputs include objective tests (e.g. PH values)and the output is based on sensory data (median ofat least 3 evaluations made by wine experts). Eachexpert graded the wine quality between 0 (very bad)and 10 (very excellent).https://archive.ics.uci.edu/ml/datasets/Wine+Quality Regression Metrics train aclassificationmodelevaluate a classificationmodelLoad Irisevaluate metricsLoad Census Datatrain aclassificationmodelevaluate a classificationmodelevaluate metricsLoad Wine Datatrain aregressionmodelevaluate a regressionmodelevaluate metricsNode 14Training Evaluate ARFF Reader Scorer CSV Reader Training Evaluate Scorer CSV Reader Training Evaluate Numeric Scorer(deprecated) CSV Writer

Nodes

Extensions

Links

Download

To use this workflow in KNIME, download it from the below URL and open it in KNIME:

Download Workflow
Created by: gabri
Created at: 2017-10-17
On NodePit since: 2024-04-30
Last update: 2024-11-21
Created with KNIME version: v4.1.3

Deploy, schedule, execute, and monitor your KNIME workflows locally, in the cloud or on-premises – with our brand new NodePit Runner.

Try NodePit Runner!