Why in Google Earth engine after SVM classification, despite that the overall accuracy is close to 80%, but the kappa coefficient shows zero? Where is the mistake?

My code is following:

var image = ee.Image(ee.ImageCollection('COPERNICUS/S2')

.filterBounds(roi)

.filterDate('2020-07-01', '2020-09-01')

.sort('CLOUD_COVER')

.first()

.clip(roi));

Map.addLayer(image, {bands: ['B8', 'B4', 'B2'],min:0, max: 3000}, 'False colour image');

print(image);

Map.addLayer(roi);

Map.centerObject(roi);

Map.addLayer(table);

// Compute the Normalized Difference Vegetation Index (NDVI).

var nir = image.select('B8');

var red = image.select('B4');

var ndvi = nir.subtract(red).divide(nir.add(red)).rename('ndvi');

// Display the result.

Map.centerObject(image, 9);

var ndviParams = {min: -1, max: 1, palette: ['blue', 'white', 'green']};

Map.addLayer(ndvi, ndviParams, 'ndvi');

// Compute the EVI using an expression.

var evi = image.expression(

'2.5 * ((NIR-RED) / (NIR + 6 * RED - 7.5* BLUE +1))', {

NIR:image.select('B4'),

RED:image.select('B3'),

BLUE:image.select('B1')

})

.rename('evi');

Map.centerObject(image, 9);

var eviParams = {min: -1, max: 1, palette: ['FF0000', '00FF00']};

Map.addLayer(evi, eviParams, 'evi');

// Compute OSAVI.

var nir = image.select('B8');

var red = image.select('B4');

var osavi = nir.subtract(red).divide(nir.add(red).add(0.16)).rename('osavi');

Map.centerObject(image, 9);

var osaviParams = {min: -1, max: 1, palette: ['red', 'white', 'blue']};

Map.addLayer(osavi, osaviParams, 'osavi');

// Compute SR index

var nir = image.select('B8');

var red = image.select('B4');

var sr = nir.divide(red).rename('sr');

image = ee.Image([image, ndvi, evi, osavi, sr]);

print(image.bandNames());

var classNames = tNO.merge(tO);

print(classNames);

var bands = ['B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B8A', 'B9', 'B10', 'B11', 'B12', 'QA10', 'QA20', 'QA60', 'ndvi', 'evi', 'osavi', 'sr'];

var training = image.select(bands).sampleRegions({

collection: classNames,

properties: ['landcover'],

scale: 30

});

print(training);

// Create an SVM classifier with custom parameters.

var classifier = ee.Classifier.libsvm({

kernelType: 'RBF',

gamma: 0.5,

cost: 10

});

// Train the classifier.

var trained = classifier.train(training, 'landcover', bands);

// Classify the image.

var classified = image.classify(trained);

//Display classification

Map.centerObject(classNames);

Map.addLayer(classified,

{min: 0, max: 1, palette: ['fffd0c', '2b8908']},

'classification');

//Merge into one FeatureCollection

var valNames = NO.merge(O);

var validation = classified.sampleRegions({

collection: valNames,

properties: ['landcover'],

scale: 30,

});

print(validation);

//Compare the landcover of your validation data against the classification result

var testAccuracy = validation.errorMatrix('landcover', 'classification');

//Print the error matrix to the console

print('Validation error matrix: ', testAccuracy);

//Print the overall accuracy to the console

print('Validation overall accuracy: ', testAccuracy.accuracy());

var Kappa = testAccuracy.kappa();

print(Kappa, 'kappa');