python重要性,使用Python的随机森林特征重要性图

I am working with RandomForestRegressor in python and I want to create a chart that will illustrate the ranking of feature importance. This is the code I used:

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

Feature Importance

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')

I receive the following error when I attempt to replicate the code with my data:

IndexError: index 6 is out of bounds for axis 1 with size 6

Also, only one feature shows up on my chart with 100% importance where there are no labels.

Any help solving this issue so I can create this chart will be greatly appreciated.

解决方案

Here is an example using the iris data set.

>>> from sklearn.datasets import load_iris

>>> iris = load_iris()

>>> rnd_clf = RandomForestClassifier(n_estimators=500, n_jobs=-1, random_state=42)

>>> rnd_clf.fit(iris["data"], iris["target"])

>>> for name, importance in zip(iris["feature_names"], rnd_clf.feature_importances_):

... print(name, "=", importance)

sepal length (cm) = 0.112492250999

sepal width (cm) = 0.0231192882825

petal length (cm) = 0.441030464364

petal width (cm) = 0.423357996355

Plotting feature importance

>>> features = iris['feature_names']

>>> importances = rnd_clf.feature_importances_

>>> indices = np.argsort(importances)

>>> plt.title('Feature Importances')

>>> plt.barh(range(len(indices)), importances[indices], color='b', align='center')

>>> plt.yticks(range(len(indices)), [features[i] for i in indices])

>>> plt.xlabel('Relative Importance')

>>> plt.show()