我正在使用python中的RandomForestRegressor，我想创建一个图表来说明特性重要性的排名。这是我使用的代码：from sklearn.ensemble import RandomForestRegressor
MT= pd.read_csv("MT_reduced.csv")
df = MT.reset_index(drop = False)
columns2 = df.columns.tolist()
# Filter the columns to remove ones we don't want.
columns2 = [c for c in columns2 if c not in["Violent_crime_rate","Change_Property_crime_rate","State","Year"]]
# Store the variable we'll be predicting on.
target = "Property_crime_rate"
# Let’s randomly split our data with 80% as the train set and 20% as the test set:
# Generate the training set. Set random_state to be able to replicate results.
train2 = df.sample(frac=0.8, random_state=1)
#exclude all obs with matching index
test2 = df.loc[~df.index.isin(train2.index)]
print(train2.shape) #need to have same number of features only difference should be obs
print(test2.shape)
# Initialize the model with some parameters.
model = RandomForestRegressor(n_estimators=100, min_samples_leaf=8, random_state=1)
#n_estimators= number of trees in forrest
#min_samples_leaf= min number of samples at each leaf
# Fit the model to the data.
model.fit(train2[columns2], train2[target])
# Make predictions.
predictions_rf = model.predict(test2[columns2])
# Compute the error.
mean_squared_error(predictions_rf, test2[target])#650.4928
特征重要性features=df.columns[[3,4,6,8,9,10]]
importances = model.feature_importances_
indices = np.argsort(importances)
plt.figure(1)
plt.title('Feature Importances')
plt.barh(range(len(indices)), importances[indices], color='b', align='center')
plt.yticks(range(len(indices)), features[indices])
plt.xlabel('Relative Importance')
尝试用数据复制代码时，收到以下错误：IndexError: index 6 is out of bounds for axis 1 with size 6
另外，在没有标签的情况下，只有一个功能显示在我的图表上，其重要性为100%。
任何帮助解决这个问题，所以我可以创建这个图表将非常感谢。
