plt.figure(dpi=120) from sklearn.linear_model import RidgeClassifier from sklearn.model_selection import train_test_split from sklearn.preprocessing import OrdinalEncoder, LabelEncoder
from yellowbrick.classifier import ROCAUC from yellowbrick.datasets import load_game
# 导入数据 X, y = load_game()
# 数据转换 X = OrdinalEncoder().fit_transform(X) y = LabelEncoder().fit_transform(y)
# 构建测试集和训练集 X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
Residuals Plot on the Concrete dataset with a Q-Q plot
展示Lasso回归模型效果
import matplotlib.pyplot as plt
plt.figure(dpi=120) from sklearn.linear_model import Lasso from yellowbrick.datasets import load_bikeshare from yellowbrick.regressor import prediction_error
X, y = load_bikeshare() visualizer = prediction_error(Lasso(), X, y)#一行代码即可展示，方不方便
更多实例见下一节～～
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
分类模型展示（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
回归模型展示（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
聚类模型展示（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
模型选择（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
目标展示（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
