---
metadata:
  - name: generator
    content: Diplodoc Platform v5.50.3
alternate:
  - https://catboost.ai/docs/en/concepts/python-reference_catboostclassifier_plot_predictions.md
---
> **Documentation Index:** Fetch the complete configuration index at https://catboost.ai/docs/en/llms.txt

# plot_predictions

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Sequentially vary the value of the specified features to put them into all buckets and calculate predictions for the input objects accordingly.

{% note alert %}

- Only models trained on datasets that do not contain categorical features are supported.
- Multiclassification modes are not supported.

{% endnote %}
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<!-- source: en/_includes/work_src/reusage-python/plot_predictions__call_format.md -->
## Method call format {#call-format}

```python
plot_predictions(data, features_to_change, plot=True, plot_file=None)
```
<!-- endsource: en/_includes/work_src/reusage-python/plot_predictions__call_format.md -->

## Parameters {#parameters}


### data

#### Description

The data to plot predictions for.

For example, use a two-document slice of the original dataset (refer to the example below).

**Possible types**

- numpy.ndarray
- pandas.DataFrame
- pandas.SparseDataFrame
- [polars.DataFrame](https://docs.pola.rs/api/python/stable/reference/dataframe/index.html)
- scipy.sparse.spmatrix
- catboost.Pool

**Default value**

Required parameter


### features_to_change

#### Description

To reduce the number of trees to use when the model is applied or the metrics are calculated, set the The list of numerical features to vary the prediction value for.

For example, chose the required features by selecting top N most important features that impact the prediction results for a pair of objects according to [PredictionDiff](https://catboost.ai/docs/en/concepts/fstr.md#fstr__prediction-diff) (refer to the example below).

**Possible types**

- list of int
- string
- combination of list of int & string

**Default value**

Required parameter


### plot

#### Description

Plot a Jupyter Notebook chart based on the calculated predictions.

**Possible types**

bool

**Default value**

True


### plot_file

#### Description

The name of the output HTML-file to save the chart to.


**Possible types**

string

**Default value**

None (the files is not saved)


## Return value {#output-format}

Dict with two fields:

- `params` — `dict` of best-found parameters.
- `cv_results` — `dict` or pandas.core.frame.DataFrame with cross-validation results. Сolumns are: `test-error-mean`, `test-error-std`, `train-error-mean`, `train-error-std`.

## Examples {#example}

```python
import numpy as np
from catboost import Pool, CatBoostClassifier

train_data = np.random.randint(0, 100, size=(100, 10))
train_label = np.random.randint(0, 2, size=(100))
train_pool = Pool(train_data, train_label)
train_pool_slice = train_pool.slice([2, 3])

model = CatBoostClassifier()
model.fit(train_pool)

prediction_diff = model.get_feature_importance(train_pool_slice,
                                               type='PredictionDiff',
                                               prettified=True)

model.plot_predictions(data=train_pool_slice,
                       features_to_change=prediction_diff["Feature Id"][:2],
                       plot=True,
                       plot_file="plot_predictions_file.html")

```

An example of the first plotted chart:
![](../images/jupyter__catboostclassifier__plot_prediction.png)