變數重要性和變數選擇in xgboost

1。變數重要得分

或者xgboost本來就有內建函式

進行排序啦，更友好

2.變數選擇

selectfrommodel

比如這樣（記得要transform之後再傳給select

# select features using threshold

selection = selectfrommodel(model, threshold=thresh, prefit=true)
select_x_train = selection.transform(x_train)
# train model
selection_model = xgbclassifier()
selection_model.fit(select_x_train, y_train)
# eval model
select_x_test = selection.transform(x_test)
y_pred = selection_model.predict(select_x_test)

# use feature importance for feature selection

from numpy import loadtxt
from numpy import sort
from xgboost import xgbclassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.feature_selection import selectfrommodel
# load data
dataset = loadtxt('pima-indians-diabetes.csv', delimiter=",")
# split data into x and y
x = dataset[:,0:8]
y = dataset[:,8]
# split data into train and test sets
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.33, random_state=7)
# fit model on all training data
model = xgbclassifier()
model.fit(x_train, y_train)
# make predictions for test data and evaluate
y_pred = model.predict(x_test)
predictions = [round(value) for value in y_pred]
accuracy = accuracy_score(y_test, predictions)
print("accuracy: %.2f%%" % (accuracy * 100.0))
# fit model using each importance as a threshold
thresholds = sort(model.feature_importances_)
for thresh in thresholds:
# select features using threshold
selection = selectfrommodel(model, threshold=thresh, prefit=true)
select_x_train = selection.transform(x_train)
# train model
selection_model = xgbclassifier()
selection_model.fit(select_x_train, y_train)
# eval model
select_x_test = selection.transform(x_test)
y_pred = selection_model.predict(select_x_test)
predictions = [round(value) for value in y_pred]
accuracy = accuracy_score(y_test, predictions)
print("thresh=%.3f, n=%d, accuracy: %.2f%%" % (thresh, select_x_train.shape[1], accuracy*100.0))

accuracy: 77.95%

thresh=0.071, n=8, accuracy: 77.95%

thresh=0.073, n=7, accuracy: 76.38%

thresh=0.084, n=6, accuracy: 77.56%

thresh=0.090, n=5, accuracy: 76.38%

thresh=0.128, n=4, accuracy: 76.38%

thresh=0.160, n=3, accuracy: 74.80%

thresh=0.186, n=2, accuracy: 71.65%

thresh=0.208, n=1, accuracy: 63.78%

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