Plot feature importance with xgboost

Question

When I plot the feature importance, I get this messy plot. I have more than 7000 variables. I understand the built-in function only selects the most important, although the final graph is unreadable. This is the complete code:

import numpy as np
import pandas as pd
df = pd.read_csv('ricerice.csv')
array=df.values
X = array[:,0:7803]
Y = array[:,7804]
from xgboost import XGBClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
seed=0
test_size=0.30
X_train, X_test, y_train, y_test = train_test_split(X,Y,test_size=test_size, random_state=seed)
from xgboost import XGBClassifier
model = XGBClassifier()
model.fit(X, Y)
import matplotlib.pyplot as plt
from matplotlib import pyplot
from xgboost import plot_importance
fig1=plt.gcf()
plot_importance(model)
plt.draw()
fig1.savefig('xgboost.png', figsize=(50, 40), dpi=1000)

Although the size of the figure, the graph is illegible.

Answer 1

There are couple of points:

1. To fit the model, you want to use the training dataset (X_train, y_train), not the entire dataset (X, y).
2. You may use the max_num_features parameter of the plot_importance() function to display only top max_num_features features (e.g. top 10).

With the above modifications to your code, with some randomly generated data the code and output are as below:

import numpy as np

# generate some random data for demonstration purpose, use your original dataset here
X = np.random.rand(1000,100)     # 1000 x 100 data
y = np.random.rand(1000).round() # 0, 1 labels

from xgboost import XGBClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
seed=0
test_size=0.30
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=test_size, random_state=seed)
from xgboost import XGBClassifier
model = XGBClassifier()
model.fit(X_train, y_train)
import matplotlib.pylab as plt
from matplotlib import pyplot
from xgboost import plot_importance
plot_importance(model, max_num_features=10) # top 10 most important features
plt.show()