我正在使用Python中的RandomForestRegressor，我想創(chuàng)建一個圖表來說明功能重要性的排名。這是我使用的代碼：

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

功能重要性 h1>

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')http://www.agcross.com/2015/02/random-forests-in-python-with-scikit-learn/上找到的示例更改了此功能重要性代碼

我嘗試使用我的數(shù)據(jù)復制代碼時收到以下錯誤：

IndexError: index 6 is out of bounds for axis 1 with size 6此外，只有一個功能出現(xiàn)在我的統(tǒng)計圖中，100%重要，沒有標簽。

任何幫助解決這個問題，所以我可以創(chuàng)建這個圖表將不勝感激。

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