# Data for Classification

{% hint style="info" %}
**You are looking at the old Evidently documentation**: this API is available with versions 0.6.7 or lower. Check the newer version [here](https://docs.evidentlyai.com/introduction).
{% endhint %}

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

## Code example

{% embed url="<https://github.com/evidentlyai/evidently/blob/main/examples/how_to_questions/how_to_use_column_mapping.ipynb>" %}

## 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          |

```python
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:

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

or

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

#### Option 2

Target: labels, Preds: labels.

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

```python
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         |

```python
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.

```python
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.

```python
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.

```python
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          |

```python
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        |

```python
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.


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