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Copy file name to clipboardExpand all lines: src/locales/en.ts
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@@ -7,7 +7,7 @@ export const en = {
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fileUploadError: 'Please upload a valid csv file.',
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removeButton: 'Remove',
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dropzoneLabel:
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'Drag and drop your csv file here, click to select one of your own files or use the "Demo dataset" button',
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'Drag and drop your csv file here, click to select a local file or use the "Demo dataset" button',
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datasetPreview: 'Dataset preview showing the first 5 rows.',
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error: 'Sorry, something went wrong.',
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loadingMessage: 'Setting up environment...',
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dataSet: 'Dataset',
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dataSetTooltip: `Preprocess your data such that:
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- missing values are removed or replaced;
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- all columns (except your bias metric column) should have the same datatypes, e.g., numerical or categorical;
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- the bias metric column is numerical`,
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performanceMetric: 'Bias metric',
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- all columns (except your outcome label column) should have the same datatypes, e.g., numerical or categorical;
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- the outcome label column is numerical`,
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performanceMetric: 'Outcome label',
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performanceMetricTooltip:
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'Clustering will be performed on the bias metrics. The bias metric should be numerical. Examples of bias metrics are "being classified as high risk" or "selected for an investigation"',
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'Clustering will be performed on the outcome labels. The outcome label should be numerical. Examples of outcome labels are "being classified as high risk" or "selected for an investigation"',
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dataType: 'Type of data',
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dataTypeTooltip:
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'Specify whether the data are categorical or numerical. All columns (except your bias metric column) should have the same data type',
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'Specify whether the data are categorical or numerical. All columns (except your outcome label column) should have the same data type',
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categoricalData: 'Categorical data',
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numericalData: 'Numerical data',
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filterSelect:
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iterations: 'Iterations',
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minClusterSize: 'Minimal cluster size',
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performanceInterpretation: {
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title: 'Bias metric interpretation',
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lower: 'Lower value of bias metric is better, such as error rate',
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higher: 'Higher value of bias metric is better, such as accuracy',
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title: 'Outcome label interpretation',
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lower: 'Lower value of outcome label is better, such as error rate',
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higher: 'Higher value of outcome label is better, such as accuracy',
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tooltip:
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'When error rate or misclassifications are chosen as the bias metric, a lower value is preferred, as the goal is to minimize errors. Conversely, when accuracy or precision is selected as the bias metric, a higher value is preferred, reflecting the aim to maximize performance.',
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'When error rate or misclassifications are chosen as the outcome label, a lower value is preferred, as the goal is to minimize errors. Conversely, when accuracy or precision is selected as the outcome label, a higher value is preferred, reflecting the aim to maximize performance.',
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},
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iterationsTooltip:
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'Number of times the dataset is split in smaller clusters until the minimal cluster size is reached',
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},
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errors: {
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csvRequired: 'Please upload a csv file.',
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targetColumnRequired: 'Please select a bias metric.',
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targetColumnRequired: 'Please select a outcome label.',
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dataTypeRequired: 'Please select a data type.',
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noNumericColumns:
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'No numeric columns found. Please upload a valid dataset.',
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},
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demoCard: {
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title: 'Try it out!',
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description: 'Alternatively, use our demo dataset.',
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description:
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"Use our demo dataset if you don't have a dataset at hand",
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},
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biasAnalysis: {
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testingStatisticalSignificance: `**4. Testing statistical significance for the bias score difference between the most deviating cluster and the rest of the dataset**
- <i class="font-serif">H</i><sub>0</sub>: no difference in bias between the most deviating cluster and the rest of the dataset
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- <i class="font-serif">H</i><sub>1</sub>: difference in bias between the most deviating cluster and the rest of the dataset
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- <i class="font-serif">H</i><sub>0</sub>: no difference in outcome labels between the most deviating cluster and the rest of the dataset
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- <i class="font-serif">H</i><sub>1</sub>: difference in outcome labels between the most deviating cluster and the rest of the dataset
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A two-sided t-test is performed to accept or reject <i class="font-serif">H</i><sub>0</sub>:.
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p_value : {{p_val}}
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`,
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dataSetPreview: {
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heading: '0. Preview of real data',
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heading: '1. Preview of data',
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},
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demo: {
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heading: 'Information about demo dataset',
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| false_positive | Defendant predicted to reoffend, but didn't | 0: no FP, 1: FP |
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In this example, we analyze which group is most adversely affected by the risk prediction algorithm. We do this by applying the clustering algorithm on the dataset previewed below. The column "is_recid" indicates whether a defendant reoffended or not (1: yes, 0: no). The "score_text" column indicates whether a defendant was predicted to reoffend (1: yes, 0: no). The column "false_positive" (FP) represents cases where a defendant was predicted to reoffended by the algorithm, but didn't do so (1: FP, 0: no FP). A preview of the data can be found below. The column "false_positive" is used as the "bias metric".
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<br>
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In this example, we analyze which group is most adversely affected by the risk prediction algorithm. We do this by applying the clustering algorithm on the dataset previewed below. The column "is_recid" indicates whether a defendant reoffended or not (1: yes, 0: no). The "score_text" column indicates whether a defendant was predicted to reoffend (1: yes, 0: no). The column "false_positive" (FP) represents cases where a defendant was predicted to reoffended by the algorithm, but didn't do so (1: FP, 0: no FP). A preview of the data can be found below. The column "false_positive" is used as the outcome label.
'The "{{variable}}" variable distribution across the different clusters:',
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'"{{variable}}" distribution across the different clusters:',
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},
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splittingDataset: {
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heading: '2. Splitting dataset',
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description: `To reduce the possibility that the clustering method detects noise, the dataset is split in a train (80%) and test dataset (20%). The clustering method is first fitted on the train dataset. Then, the presence of statistically significant bias in the most deviating clusters is evaluated using the test dataset.`,
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heading: '3. Splitting dataset',
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description: `To reduce the possibility that the clustering method detects noise, the dataset is split in a train (80%) and test dataset (20%). The clustering method is first fitted on the train dataset. Then, the presence of statistically significant signal in the most deviating clusters is evaluated using the test dataset.`,
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},
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clusterinResults: {
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heading: '3. Clustering results',
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heading: '4. Clustering results',
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description: `
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- Number of clusters detected: {{clusterCount}}
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`,
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label: 'Choose cluster to show number of datapoints for',
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valueText: 'Number of datapoints in cluster {{index}}: {{value}}',
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},
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higherAverage: `The most biased cluster has a statistically significant higher average bias score than the rest of the dataset.`,
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noSignificance: `No statistically significant difference in average bias score between the most biased cluster and the rest of the dataset.`,
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higherAverage: `The most deviating cluster has statistically significant different outcome labels than the rest of the dataset.`,
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noSignificance: `No statistically significant difference in outcome labels between the most biased cluster and the rest of the dataset.`,
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conclusion: `6. Conclusion and bias report`,
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conclusion: `7. Conclusion and bias report`,
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conclusionDescription: `From the above figures and statistical tests, it can be concluded that:`,
Copy file name to clipboardExpand all lines: src/locales/nl.ts
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@@ -193,20 +193,33 @@ export const nl = {
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biasAnalysis: {
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demo: {
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heading: 'Informatie over de demodataset',
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description:
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'Als demo wordt de [Twitter15](https://www.dropbox.com/scl/fi/flgahafqckxtup2s9eez8/rumdetect2017.zip?dl=0&e=1&file_subpath=%2Frumor_detection_acl2017%2Ftwitter15&rlkey=b7v86v3q1dpvcutxqk0xi7oej) dataset hieronder geladen. De dataset bevat kenmerken van tweets en de voorspelling van een BERT-gebaseerd misinformatie detectie algoritme of een tweet nepnieuws is of niet. Fout-positieve classificaties zijn gemarkeerd als FP. Een FP geeft aan dat tweet ten onrechte door het misinformatie algoritme zijn geclassificeerd als nepnieuws. De FP-metriek wordt in dit voorbeeld gebruikt als metriek om bias te meten. \n \n \n\n In dit voorbeeld onderzoeken we welk type tweets vaker/minder vaak door het misinformatie algoritme worden geclassificeerd als nepnieuws.',
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},
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testingStatisticalSignificance: `**4. Testing statistical significance for the bias score difference between the most deviating cluster and the rest of the dataset**
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description: `Als demonstratie wordt de [COMPAS (Correctional Offender Management Profiling for Alternative Sanctions) dataset](https://github.com/propublica/compas-analysis/tree/master) geladen. De dataset bevat kenmerken van criminele verdachten en hun risico op recidive, zoals voorspeld door het COMPAS-algoritme. De dataset bevat demografische gegevens zoals leeftijd, geslacht en ras, evenals strafblad, details over de aanklacht en het voorspelde risicolabel. Deze dataset wordt gebruikt als benchmark voor het bestuderen van algoritmische discriminatie. Een beschrijving van alle variabelen is te vinden in de onderstaande tabel.
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**Variabelebeschrijving**
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- <i class="font-serif">H</i><sub>0</sub>: no difference in bias between the most deviating cluster and the rest of the dataset
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- <i class="font-serif">H</i><sub>1</sub>: difference in bias between the most deviating cluster and the rest of the dataset
| c_charge_degree | Ernst van de strafrechtelijke aanklacht | M: Overtreding – Minder ernstige feiten, F: Misdrijf – Ernstigere feiten |
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| is_recid | Of de verdachte opnieuw de fout in ging (recidive) | 0: Nee, 1: Ja |
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| score_text | Voorspeld risicolabel van de verdachte | 0: Geen hoog risico, 1: Hoog risico |
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| false_positive | Verdachte voorspeld om te recidiveren, maar deed dat niet | 0: geen valse positieve, 1: valse positieve |
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A two-sided t-test is performed to accept or reject <i class="font-serif">H</i><sub>0</sub>:.
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<br>
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In dit voorbeeld analyseren we welke groep het meest nadelig wordt beïnvloed door het risicovoorspellingsalgoritme. Dit doen we door het clusteralgoritme toe te passen op de onderstaande datasetweergave. De kolom "is_recid" geeft aan of een verdachte daadwerkelijk opnieuw de fout in ging (1: ja, 0: nee). De kolom "score_text" geeft aan of werd voorspeld dat een verdachte opnieuw de fout in zou gaan (1: ja, 0: nee). De kolom "false_positive" (FP) vertegenwoordigt gevallen waarin het algoritme voorspelde dat een verdachte opnieuw de fout in zou gaan, maar dit niet gebeurde (1: FP, 0: geen FP). Een voorbeeldweergave van de gegevens is hieronder te vinden. De kolom "false_positive" wordt gebruikt als uitkomstlabel.
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`,
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},
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testingStatisticalSignificance: `**5. Testen van clusterverschillen ten opzichte van uitkomstlabels**
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- <i class="font-serif">H</i><sub>0</sub>: er is geen verschil in uitkomstlabels tussen het meest afwijkende cluster en de rest van de dataset
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- <i class="font-serif">H</i><sub>1</sub>: er is een verschil in uitkomstlabels tussen het meest afwijkende cluster en de rest van de dataset
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T_statistic : {{t_stat}}
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Er wordt een tweezijdige t-toets uitgevoerd om <i class="font-serif">H</i><sub>0</sub> te aanvaarden of te verwerpen.
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p_value : {{p_val}}
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p-waarde : {{p_val}}
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`,
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parameters: {
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heading: 'Geselecteerde parameters',
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- Gegevenstype: {{dataType}}
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`,
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},
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distribution: {
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mainHeading:
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'6. Testen van clusterverschillen ten opzichte van kenmerken',
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heading: '"{{variable}}" verdeling over de verschillende clusters:',
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},
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splittingDataset: {
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heading: '3. Splitsen dataset',
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description: `Om de kans te verkleinen dat de clusteringmethode ruis detecteert, wordt de dataset opgesplitst in een trainingsset (80%) en een testset (20%). De clusteringmethode wordt eerst getraind op de trainingsset. Vervolgens wordt met behulp van de testset beoordeeld of er sprake is van een statistisch significant signaal in de meest afwijkende clusters.`,
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