---
metadata:
  - name: generator
    content: Diplodoc Platform v5.52.0
alternate:
  - https://catboost.ai/docs/en/concepts/python-reference_catboostipythonwidget.md
  - href: en/concepts/python-reference_catboostipythonwidget.md
    type: text/markdown
    title: Markdown version
  - href: ../llms.txt
    type: text/markdown
    title: llms.txt
---
> **Documentation Index:** Fetch the complete configuration index at https://catboost.ai/docs/en/llms.txt

# MetricVisualizer

```python
class MetricVisualizer(train_dirs,
                       subdirs=False)
```

## Purpose {#purpose}

Plot information regarding training, metric evaluations or cross-validation runs on a single chart. One chart can contain information regarding one or several runs depending on the input information. The charts can be either plotted in real time while the trainings are performed, or after the trainings are over.

## Parameters {#parameters}
### train_dirs

#### Description

The directory or the list of directories to read the files generated during training.

**Possible types**

- string
- list of strings

**Default value**

catboost_info

### subdirs

#### Description

Gather and read data from the specified directories and all subdirectories.

**Possible types**

bool

**Default value**

False (the data for charts is gathered from the specified directories only)

## Methods {#methods}

Method: [start](https://catboost.ai/docs/en/concepts/python-reference_catboostipythonwidget_update_widget.md)

<!-- source: en/_includes/work_src/reusage/catboostipythonwidget__purpose.md -->
Plot metrics for all training, metric evaluations and cross-validation runs that have logs in the given directory.
<!-- endsource: en/_includes/work_src/reusage/catboostipythonwidget__purpose.md -->



> # Gather data from the specified directory only
>
> 1. Train a model from the root of the file system (`/`):
>     ```python
>     from catboost import CatBoostClassifier
>
>     cat_features = [0,1,2]
>
>     train_data = [["a", "b", 1, 4, 5, 6],
>     ["a", "b", 4, 5, 6, 7],
>     ["c", "d", 30, 40, 50, 60]]
>
>     train_labels = [1,1,0]
>
>     model = CatBoostClassifier(iterations=20,
>     loss_function = "CrossEntropy",
>     train_dir = "crossentropy")
>
>     model.fit(train_data, train_labels, cat_features)
>     predictions = model.predict(train_data)
>     ```
>
> 1. Plot a chart using the information regarding the previous training (from the `crossentropy` directory):
>     ```
>     import catboost
>
>     w = catboost.MetricVisualizer('/crossentropy/')
>     w.start()
>     ```
>
>     <!-- source: en/_includes/work_src/reusage-code-examples/graph-plotted-with-jupyter-notebook.md -->
>     The following is a chart plotted with [Jupyter Notebook](https://catboost.ai/docs/en/features/visualization_jupyter-notebook.md) for the given example.
>     <!-- endsource: en/_includes/work_src/reusage-code-examples/graph-plotted-with-jupyter-notebook.md -->
>
>     ![](../images/interface__visualization-tools__jupyter__cross-entropy.png)

> # Gather and read data from all subdirectories
>
> 1. Train two models from the root of the file system (`/`):
>
>     ```python
>     from catboost import CatBoostClassifier
>
>     cat_features = [0,1,2]
>
>     train_data = [["a", "b", 1, 4, 5, 6],
>     ["a", "b", 4, 5, 6, 7],
>     ["c", "d", 30, 40, 50, 60]]
>
>     train_labels = [1,1,0]
>
>     model = CatBoostClassifier(iterations=20,
>     loss_function = "CrossEntropy",
>     train_dir = "crossentropy")
>
>     model.fit(train_data, train_labels, cat_features)
>     predictions = model.predict(train_data)
>     ```
>
>     ```python
>     from catboost import CatBoostClassifier
>
>     cat_features = [0,1,2]
>
>     train_data = [["a", "b", 1, 4, 5, 6],
>     ["a", "b", 4, 5, 6, 7],
>     ["c", "d", 30, 40, 50, 60]]
>
>     train_labels = [1,1,0]
>
>     model = CatBoostClassifier(iterations=20,
>     train_dir = "logloss")
>
>     model.fit(train_data, train_labels, cat_features)
>     predictions = model.predict(train_data)
>     ```
>
> 1. Plot charts using the information from all subdirectories (`crossentropy` and `logloss`) of the root of the file system:
>     ```python
>     import catboost
>
>     w = catboost.MetricVisualizer('/', subdirs=True)
>     w.start()
>     ```
>
>     <!-- source: en/_includes/work_src/reusage-code-examples/graph-plotted-with-jupyter-notebook.md -->
>     The following is a chart plotted with [Jupyter Notebook](https://catboost.ai/docs/en/features/visualization_jupyter-notebook.md) for the given example.
>     <!-- endsource: en/_includes/work_src/reusage-code-examples/graph-plotted-with-jupyter-notebook.md -->
>
>     ![](../images/interface__visualization-tools__jupyter__cross-entropy_and_logloss.png)