Yellowbrick.model_selection 不适用于回归，但适用于分类答案

【问题标题】：yellowbrick.model_selection does not work for Regression but works for ClassificationYellowbrick.model_selection 不适用于回归，但适用于分类
【发布时间】：2021-05-24 05:07:57
【问题描述】：

我有一个具有 spotify 数据功能的数据框 df。当我使用 RandomForestClassifier 运行模型时，我得到了重要的特征图，但是当我运行 RandomForestRegressor 时，我只得到了一个反对流行度的栏。有人可以帮忙吗？

from yellowbrick.model_selection import FeatureImportances

# Load the classification data set
X = df[features]
y = df.popularity

train_X, test_X, train_y, test_y = train_test_split(X,y, test_size= 0.1, random_state=38)

model = RandomForestClassifier(n_estimators=10)
# model = RandomForestRegressor(n_estimators=10)

viz = FeatureImportances(model)
viz.fit(X, y)
viz.show()

【问题讨论】：

标签： python random-forest yellowbrick

【解决方案1】：

我用 spotify 数据集重复了上述实验，但是我能够将 RandomForestRegressor 与 Yellowbrick 的 FeatureImportances Visualizer 一起使用（见下图）。我建议您将 Yellowbrick 更新到 2 月 9 日最近发布的最新版本。 pip install -U Yellowbrick

from yellowbrick.model_selection import FeatureImportances
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor

# Load spotify Data Set
df = pd.read_csv('data.csv.zip')

df = df[['acousticness', 'danceability', 'duration_ms', 'energy',
         'explicit', 'instrumentalness', 'liveness', 'loudness',
         'popularity','speechiness', 'tempo']]

X = df.drop('popularity', axis=1)
y = df.popularity

train_X, test_X, train_y, test_y = train_test_split(X,y, test_size= 0.1, random_state=38)

#model = RandomForestClassifier(n_estimators=10)
model = RandomForestRegressor(n_estimators=10)

viz = FeatureImportances(model)
viz.fit(X, y)
viz.show()

【讨论】：