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        "author": "Hiroyuki Yamasaki",
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        "description": "# predictiveness-curve\n\n[![PyPi version](https://img.shields.io/pypi/v/predictiveness-curve.svg)](https://pypi.python.org/pypi/predictiveness-curve/)  [![Python support](https://img.shields.io/badge/python-3.6+-blue.svg)](https://www.python.org/downloads/release/python-360/) [![License](https://img.shields.io/github/license/yamasakih/predictiveness-curve.svg)](https://github.com/yamasakih/predictiveness-curve/blob/master/LICENSE) [![Build Status](https://travis-ci.org/yamasakih/predictiveness-curve.svg?branch=master)](https://travis-ci.org/yamasakih/predictiveness-curve) [![codecov](https://codecov.io/gh/yamasakih/predictiveness-curve/branch/master/graph/badge.svg)](https://codecov.io/gh/yamasakih/predictiveness-curve) [![Downloads](https://pepy.tech/badge/predictiveness-curve)](https://pepy.tech/project/predictiveness-curve) [![Downloads](https://pepy.tech/badge/predictiveness-curve/month)](https://pepy.tech/project/predictiveness-curve/month) [![Downloads](https://pepy.tech/badge/predictiveness-curve/week)](https://pepy.tech/project/predictiveness-curve/week)\n\n## What's Predictiveness Curve?\nPredictiveness curve is a method to display two graphs simultaneously. In both figures, the x-axis is risk percentile, the y-axis of one figure is the value of risk, and the y-axis of the other figure is true positive fractions. This makes it possible to visualize whether the model of risk fits in the medical field and which value of risk should be used as the basis for the model. See [Am. J. Epidemiol. 2008; 167:362\u2013368](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2939738/) for details.\n\n## Install\n\nThis module implements functions to plot `Predictiveness Curve`.  \nInstall with :\n\n`pip install predictiveness-curve`\n\n## Example\n\n```python\nfrom predictiveness_curve import plot_predictiveness_curve\nfrom sklearn.datasets import load_breast_cancer\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.model_selection import train_test_split\n\ndata = load_breast_cancer()\ny = data.target\nX = data.data\n\ntraining_X, test_X, training_y, test_y = train_test_split(\n    X, y, test_size=0.5, random_state=42)\n\nclsf = RandomForestClassifier(n_estimators=100, random_state=42)\nclsf.fit(training_X, training_y)\nprobabilities = clsf.predict_proba(test_X)[:, 1]\n\nplot_predictiveness_curve(probabilities, test_y)\n```\n\nSee [notebooks directory](https://github.com/yamasakih/predictiveness-curve/tree/master/notebooks) for details.",
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