Data for Classification

How to define the data schema for classification.

To evaluate classification model performance, you must correctly map the input data schema.

Code example

evidently/examples/how_to_questions/how_to_use_column_mapping.ipynb at main · evidentlyai/evidentlyGitHub

Column Mapping

To evaluate the classification performance, you need both true labels and prediction. Depending on the classification type (e.g., binary, multi-class, probabilistic), you have different options of how to pass the predictions.

Multiclass classification

Option 1

Target: encoded labels, Preds: encoded labels + Optional[target_names].

target	prediction
1	1
0	2
…	…
2	2

column_mapping = ColumnMapping()

column_mapping.target = 'target'
column_mapping.prediction = 'prediction'
column_mapping.target_names = ['Setosa', 'Versicolour', 'Virginica']

If you pass the target names, they will appear on the visualizations.

You can also pass the target names as a dictionary:

column_mapping.target_names = {'0':'Setosa', '1':'Versicolor', '2':'Virginica'}

or

column_mapping.target_names = {0:'Setosa', 1:'Versicolor', 2:'Virginica'} 

Option 2

Target: labels, Preds: labels.

target	prediction
‘Versicolour’	‘Versicolour’
‘Setosa’	‘Virginica’
…	…
‘Virginica’	‘Virginica’

column_mapping = ColumnMapping()

column_mapping.target = 'target'
column_mapping.prediction = 'prediction'

Multiclass probabilistic classification

Target: labels, Preds: columns named after labels.

target	‘Versicolour’	‘Setosa’	‘Virginica’
‘Setosa’	0.98	0.01	0.01
‘Virginica’	0.5	0.2	0.3
…	…
‘Virginica’	0.2	0.7	0.1

column_mapping = ColumnMapping()

column_mapping.target = 'target'
column_mapping.prediction = ['Setosa', 'Versicolour', 'Virginica']

Naming the columns after the labels is a requirement. You cannot pass a custom list.

Binary classification

Option 1

Target: encoded labels, Preds: encoded labels + pos_label + Optional[target_names]

target	prediction
1	1
0	1
…	…
1	0

By default, Evidently expects the positive class to be labeled as ‘1’. If you have a different label, specify it explicitly.

column_mapping = ColumnMapping()

column_mapping.target = 'target'
column_mapping.prediction = 'prediction'
column_mapping.target_names = ['churn', 'not_churn']
column_mapping.pos_label = 0

If you pass the target names, they will appear on the visualizations.

Option 2

Target: labels, Preds: labels + pos_label

target	prediction
‘churn’	‘churn’
‘not_churn’	‘churn’
…	…
‘churn’	‘not_churn’

Passing the name of the positive class is a requirement in this case.

column_mapping = ColumnMapping()

column_mapping.target = 'target'
column_mapping.prediction = 'prediction'
column_mapping.pos_label = 'churn'

Binary probabilistic classification

Option 1

Target: labels, Preds: columns named after labels + pos_label

target	‘churn’	‘not_churn’
‘churn’	0.9	0.1
‘churn’	0.7	0.3
…	…
‘not_churn’	0.5	0.5

Passing the name of the positive class is a requirement in this case.

column_mapping = ColumnMapping()

column_mapping.target = 'target'
column_mapping.prediction = ['churn', 'not_churn']
column_mapping.pos_label = 'churn'

Option 2

Target: labels, Preds: a column named like one of the labels + pos_label

target	‘not_churn’
‘churn’	0.5
‘not_churn’	0.1
…	…
‘churn’	0.9

column_mapping = ColumnMapping()

column_mapping.target = 'target'
column_mapping.prediction = 'not_churn'
column_mapping.pos_label = 'churn'

Both naming the column after one of the labels and passing the name of the positive class are requirements.

Option 3

Target: encoded labels, Preds: one column with any name + pos_label

target	prediction
1	0.5
1	0.1
…	…
0	0.9

column_mapping = ColumnMapping()

column_mapping.target = 'target'
column_mapping.prediction = 'prediction'
column_mapping.pos_label = 1
column_mapping.target_names = ['churn', 'not_churn']

If you pass the target names, they will appear on the visualizations.