python重要性_基于Python的随机森林特征重要性图

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。