使用 xgboost 绘制特征重要性

当我绘制特征重要性时，我得到了这个混乱的图。我有超过 7000 个变量。我知道内置函数只选择最重要的，尽管最终的图表不可读。 这是完整的代码：

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.

有几点：

1. 为了拟合模型，您需要使用训练数据集（X_train, y_train），而不是整个数据集（X, y).
2. 您可以使用max_num_features的参数plot_importance()仅显示顶部的功能max_num_features功能（例如前 10 名）。

对代码进行上述修改后，使用一些随机生成的数据，代码和输出如下：

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()