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Scikit Plot

Scikit Plot is a Python library that provides plotting functions for visualizing scikit-learn objects and machine-learning workflows, including model evaluation curves and classification-result plots.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Scikit Plot — OpenViST project profile in AI Data & Analytics.
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Understand the project

Practical overview

When it fits

Use Scikit Plot when a Python machine-learning workflow needs evaluation charts or visual summaries, especially when you want a plotting helper rather than building each chart from scratch.

Who it is for

  • Python practitioners who use scikit-learn and want plotting functions for model analysis.
  • Machine-learning developers using other classifiers who can supply the inputs required by a plotting function.

Documented uses

  • Generate ROC and precision-recall curves from labels and predicted probabilities.
  • Turn classification results into visual summaries such as labeled confusion-matrix heatmaps.
  • Add plots to analysis or experiment reporting with less plotting boilerplate.
Before you choose

Requirements & limits

  • The README lists Scikit-learn and Matplotlib as installation dependencies.
  • Some plotting functions can accept suitable inputs from non-scikit-learn classifiers; the README demonstrates a Keras precision-recall example.
  • The project provides plot parameters for customization and links to documentation and repository examples.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install Scikit Plot for a Python analysis

Install the library after preparing its listed dependencies.

Before you start

  • Python environment
  • Scikit-learn
  • Matplotlib

Steps

  1. Install the Scikit-learn and Matplotlib dependencies.

  2. Run the command below, or use conda install -c conda-forge scikit-plot in a conda environment.

    Documented command
    pip install scikit-plot

Expected outcome

Scikit Plot is installed for use in a Python analysis.

Guide limitations

  • The installation instructions do not describe executing a plotting workflow.
Documentation 1 ↗
Source-based practical guide

Plot multiclass ROC curves from a trained classifier

Create a multiclass ROC visualization from test labels and predicted probabilities.

Before you start

  • Scikit-learn and Matplotlib
  • A trained classifier and test data
  • Ground-truth test labels and predicted probabilities

Steps

  1. Load a dataset and split it into training and test sets.

  2. Fit a classifier on the training data and obtain predicted probabilities for the test data.

  3. Import matplotlib.pyplot and scikitplot as skplt.

  4. Call skplt.metrics.plot_roc(y_test, predicted_probas) and plt.show().

Expected outcome

A ROC plot showing per-class curves, with micro- and macro-averaged curves in the documented example.

Guide limitations

  • The README's example uses a multiclass Naive Bayes classifier and the scikit-learn digits dataset.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
14 October 2026
Original website title
reiinakano/scikit-plot · Official homepage title · retained in its source language
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