Plot importance variables xgboost Python
There are couple of points:
- To fit the model, you want to use the training dataset (
X_train, y_train
), not the entire dataset (X, y
). - You may use the
max_num_features
parameter of theplot_importance()
function to display only topmax_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()