• 本文分享機器學習工具Scikit-Learn強力擴展yellowbrick。

• 通過幾行代碼可視化特征值、模型、模型評估等，幫助更便捷的的選擇機器學習模型和調參，依賴Matplotlib和Scikit-Learn。

本文目錄

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yellowbrick安裝

#?清華源加速安裝 pip?install?yellowbrick?-i?https://pypi.tuna.tsinghua.edu.cn/simple

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yellowbrick核心“武器”?-?Visualizers

Visualizers可以理解為一個scikit-learn的估計器（estimator）對象，但是附加了可視化的屬性，使用過程與使用scikit-learn模型類似：

• 導入特定的visualizers；

• 實例化visualizers；

• 擬合visualizers；

• 可視化展示。

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yellowbrick實例快速上手

• 展示ROC曲線，評估不同模型效果

import?matplotlib.pyplot?as?pltplt.figure(dpi=120) from?sklearn.linear_model?import?RidgeClassifier from?sklearn.model_selection?import?train_test_split from?sklearn.preprocessing?import?OrdinalEncoder,?LabelEncoderfrom?yellowbrick.classifier?import?ROCAUC from?yellowbrick.datasets?import?load_game#?導入數據 X,?y?=?load_game()#?數據轉換 X?=?OrdinalEncoder().fit_transform(X) y?=?LabelEncoder().fit_transform(y)#?構建測試集和訓練集 X_train,?X_test,?y_train,?y_test?=?train_test_split(X,?y,?random_state=42)#?實例化分類模型和visualizer model?=?RidgeClassifier() visualizer?=?ROCAUC(model,?classes=["win",?"loss",?"draw"])visualizer.fit(X_train,?y_train)??#?擬合visualizer visualizer.score(X_test,?y_test)??#?評價模型在訓練集上效果 visualizer.show()

• 特征工程中，展示PCA降維效果

import?matplotlib.pyplot?as?pltplt.figure(dpi=120) from?yellowbrick.features?import?PCAX,?y?=?load_credit() classes?=?['account?in?default',?'current?with?bills']visualizer?=?PCA(scale=True,?projection=3,?classes=classes) visualizer.fit_transform(X,?y) visualizer.show()

• 回歸模型中，展示預測值和真實值之間的殘差，Q-Q plot評估模型效果。

from?sklearn.linear_model?import?Ridge from?sklearn.model_selection?import?train_test_splitfrom?yellowbrick.datasets?import?load_concrete from?yellowbrick.regressor?import?ResidualsPlot#?導入數據 X,?y?=?load_concrete()#?構建訓練集、測試集 X_train,?X_test,?y_train,?y_test?=?train_test_split(X,?y,?test_size=0.2,?random_state=42)#?實例化模型和visualizer model?=?Ridge() visualizer?=?ResidualsPlot(model,?hist=False,?qqplot=True) visualizer.fit(X_train,?y_train) visualizer.score(X_test,?y_test) visualizer.show()Residuals Plot on the Concrete dataset with a Q-Q plot

• 展示Lasso回歸模型效果

import?matplotlib.pyplot?as?pltplt.figure(dpi=120) from?sklearn.linear_model?import?Lasso from?yellowbrick.datasets?import?load_bikeshare from?yellowbrick.regressor?import?prediction_errorX,?y?=?load_bikeshare() visualizer?=?prediction_error(Lasso(),?X,?y)#一行代碼即可展示，方不方便

更多實例見下一節～～

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yellowbrick常用的Visualizers

特征展示（Feature Visualization）

• Rank Features: pairwise ranking of features to detect relationships

• Parallel Coordinates: horizontal visualization of instances

• Radial Visualization: separation of instances around a circular plot

• PCA Projection: projection of instances based on principal components

• Manifold Visualization: high dimensional visualization with manifold learning

• Joint Plots: direct data visualization with feature selection

分類模型展示（Classification Visualization）

• Class Prediction Error: shows error and support in classification

• Classification Report: visual representation of precision, recall, and F1

• ROC/AUC Curves: receiver operator characteristics and area under the curve

• Precision-Recall Curves: precision vs recall for different probability thresholds

• Confusion Matrices: visual description of class decision making

• Discrimination Threshold: find a threshold that best separates binary classes

回歸模型展示（Regression Visualization）

• Prediction Error Plot: find model breakdowns along the domain of the target

• Residuals Plot: show the difference in residuals of training and test data

• Alpha Selection: show how the choice of alpha influences regularization

• Cook’s Distance: show the influence of instances on linear regression

聚類模型展示（Clustering Visualization）

• K-Elbow Plot: select k using the elbow method and various metrics

• Silhouette Plot: select k by visualizing silhouette coefficient values

• Intercluster Distance Maps: show relative distance and size/importance of clusters

模型選擇（Model Selection Visualization）

• Validation Curve: tune a model with respect to a single hyperparameter

• Learning Curve: show if a model might benefit from more data or less complexity

• Feature Importances: rank features by importance or linear coefficients for a specific model

• Recursive Feature Elimination: find the best subset of features based on importance

目標展示（Target Visualization）

• Balanced Binning Reference: generate a histogram with vertical lines showing the recommended value point to bin the data into evenly distributed bins

• Class Balance: see how the distribution of classes affects the model

• Feature Correlation: display the correlation between features and dependent variables

文本展示（Text Visualization）

• Term Frequency: visualize the frequency distribution of terms in the corpus

• t-SNE Corpus Visualization: use stochastic neighbor embedding to project documents

• Dispersion Plot: visualize how key terms are dispersed throughout a corpus

• UMAP Corpus Visualization: plot similar documents closer together to discover clusters

• PosTag Visualization: plot the counts of different parts-of-speech throughout a tagged corpus

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yellowbrick圖形個性化設置

https://www.scikit-yb.org/en/latest/index.html

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