scikitplot.seaborn#
scikitplot.seaborn#
Seaborn-style scikit-plots plotting.
This submodule provides a seaborn-like, high-level plotting API for machine-learning model exploration.
User guide. See the Seaborn-style model plots section for further details.
.api into .seaborn#
User guide. See the AUC and evaluation and AUC and evaluation sections for further details.
Plot PR or ROC curves with a seaborn-like API. |
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Visualization of the Confusion Matrix [Ra7e29df24177-1] alongside a text report showing key classification metrics. |
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Model Instance and/or Attributes Visualization. |
.decile.kds into .seaborn#
Decile-based [2] model evaluation module (Lift, Gains, KS statistics).
The _decile module includes plots for machine learning
evaluation decile analysis e.g. Gain, Lift and Decile charts, etc.
In descriptive statistics, a decile is any of the nine values that divide the sorted data into ten equal parts, so that each part represents 1/10 of the sample or population. A decile is one possible form of a quantile; others include the quartile and percentile. A decile rank arranges the data in order from lowest to highest and is done on a scale of one to ten where each successive number corresponds to an increase of 10 percentage points. See [1] [2] for more details.
References
User guide. See the Decile and model plots section for further details.
Given binary labels y_true (0/1) and probabilities y_score 1d array, compute/plot a decile [Rd1ed195c7ca1-2] table. |
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Pretty-print the legend of decile table column names. |