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sklearn-evaluation

Introduction

Quick Start

Classification
Regression
Clustering

Classification

Evaluation
Comparison
Optimization
Experiment tracking
Report generation
Evaluating class imbalance
Probability Calibration
Report: Evaluation
Report: Comparison
Interactive Confusion Matrix
Micro and Macro Averaging

Regression

Evaluate Regression

Clustering

Optimal number of clusters

Model comparison

Experiment tracking
Real-time tracking
Notebooks as experiments
Querying notebooks with SQL

Model optimization

Feature Ranking
Feature Selection
Underfitting and Overfitting

Dimensionality Reduction

Principal Component Analysis

API Reference

sklearn_evaluation.plot
sklearn_evaluation.table
sklearn_evaluation.grid
ClassifierEvaluator
Training
Experiment tracking
Notebooks

Reference

Functional vs OOP
Advanced usage using matplotlib
Other projects
Community
Developer guide
Developer API

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.md

Contents

Installation
Quick start

Introduction

Contents

Installation
Quick start

Introduction#

Installation#

pip install sklearn-evaluation

Quick start#

Classification
Regression
Clustering

next

Classification

By Ploomber
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