Random Forest Feature Importance Chart using Python : <p>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:</p>

<pre><code>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
</code></pre>

<h1>Feature Importance</h1>

<pre><code>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')
</code></pre>

<p>This feature importance code was altered from an example found on <a href="http://www.agcross.com/2015/02/random-forests-in-python-with-scikit-learn/" rel="noreferrer">http://www.agcross.com/2015/02/random-forests-in-python-with-scikit-learn/</a></p>

<p>I receive the following error when I attempt to replicate the code with my data:</p>

<pre><code>  IndexError: index 6 is out of bounds for axis 1 with size 6
</code></pre>

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

<p>Any help solving this issue so I can create this chart will be greatly appreciated.</p>
