python特征重要性_在Python中使⽤Keras的神经⽹络中的特
征重要性图
I am using python(3.6) anaconda (64 bit) spyder (3.1.2). I already set a neural network model using keras (2.0.6) for a regression problem(one response, 10 variables). I was wondering how can I generate feature importance chart like so:
def base_model():
model = Sequential()
model.add(Dense(200, input_dim=10, kernel_initializer='normal', activation='relu'))
model.add(Dense(1, kernel_initializer='normal'))
modelpile(loss='mean_squared_error', optimizer = 'adam')
return model
clf = KerasRegressor(build_fn=base_model, epochs=100, batch_size=5,verbose=0)
clf.fit(X_train,Y_train)
spyder python下载解决⽅案
I was recently looking for the answer to this question and found something that was useful for what I was doing and thought it would be helpful to share. I ended up using a permutation importance module from the eli5 package. It most easily works with a scikit-learn model. Luckily, Keras provides a wrapper for sequential models. As shown in the code below, using it is very straightforward.
from keras.wrappers.scikit_learn import KerasClassifier, KerasRegressor
import eli5
from eli5.sklearn import PermutationImportance
def base_model():
model = Sequential()
...
return model
X = ...
y = ...
my_model = KerasRegressor(build_fn=base_model, **sk_params)
my_model.fit(X,y)
perm = PermutationImportance(my_model, random_state=1).fit(X,y)
eli5.show_weights(perm, feature_names = list())

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