Package: yellowbrick
YellowbrickYellowbrick is a suite of visual analysis and diagnostic tools designed to facilitate machine learning with scikit-learn. The library implements a new core API object, theVisualizerthat is an scikit-learn estimator — an object that learns from data. Similar to transformers or models, visualizers learn from data by creating a visual representation of the model selection workflow.Visualizer allow users to steer the model selection process, building intuition around feature engineering, algorithm selection and hyperparameter tuning. For instance, they can help diagnose common problems surrounding model complexity and bias, heteroscedasticity, underfit and overtraining, or class balance issues. By applying visualizers to the model selection workflow, Yellowbrick allows you to steer predictive models toward more successful results, faster.The full documentation can be found atscikit-yb.organd includes aQuick Start Guidefor new users.VisualizersVisualizers are estimators — objects that learn from data — whose primary objective is to create visualizations that allow insight into the model selection process. In scikit-learn terms, they can be similar to transformers when visualizing the data space or wrap a model estimator similar to how theModelCV(e.g.RidgeCV,LassoCV) methods work. The primary goal of Yellowbrick is to create a sensical API similar to scikit-learn. Some of our most popular visualizers include:Classification VisualizationClassification Report: a visual classification report that displays a model's precision, recall, and F1 per-class scores as a heatmapConfusion Matrix: a heatmap view of the confusion matrix of pairs of classes in multi-class classificationDiscrimination Threshold: a visualization of the precision, recall, F1-score, and queue rate with respect to the discrimination threshold of a binary classifierPrecision-Recall Curve: plot the precision vs recall scores for different probability thresholdsROCAUC: graph the receiver operator characteristic (ROC) and area under the curve (AUC)Clustering VisualizationIntercluster Distance Maps: visualize the relative distance and size of clustersKElbow Visualizer: visualize cluster according to the specified scoring function, looking for the "elbow" in the curve.Silhouette Visualizer: selectkby visualizing the silhouette coefficient scores of each cluster in a single modelFeature VisualizationManifold Visualization: high-dimensional visualization with manifold learningParallel Coordinates: horizontal visualization of instancesPCA Projection: projection of instances based on principal componentsRadViz Visualizer: separation of instances around a circular plotRank Features: single or pairwise ranking of features to detect relationshipsModel Selection VisualizationCross Validation Scores: display the cross-validated scores as a bar chart with the average score plotted as a horizontal lineFeature Importances: rank features based on their in-model performanceLearning Curve: show if a model might benefit from more data or less complexityRecursive Feature Elimination: find the best subset of features based on importanceValidation Curve: tune a model with respect to a single hyperparameterRegression VisualizationAlpha Selection: show how the choice of alpha influences regularizationCook's Distance: show the influence of instances on linear regressionPrediction Error Plots: find model breakdowns along the domain of the targetResiduals Plot: show the difference in residuals of training and test dataTarget VisualizationBalanced Binning Reference: generate a histogram with vertical lines showing the recommended value point to the bin data into evenly distributed binsClass Balance: show the relationship of the support for each class in both the training and test data by displaying how frequently each class occurs as a bar graph the frequency of the classes' representation in the datasetFeature Correlation: visualize the correlation between the dependent variables and the targetText VisualizationDispersion Plot: visualize how key terms are dispersed throughout a corpusPosTag Visualizer: plot the counts of different parts-of-speech throughout a tagged corpusToken Frequency Distribution: visualize the frequency distribution of terms in the corpust-SNE Corpus Visualization: uses stochastic neighbor embedding to project documentsUMAP Corpus Visualization: plot similar documents closer together to discover clusters... and more! Yellowbrick is adding new visualizers all the time so be sure to check out our [examples gallery]https://github.com/DistrictDataLabs/yellowbrick/tree/develop/examples) — or even thedevelopbranch — and feel free to contribute your ideas for new Visualizers!Affiliations