Most important features Gaussian Naive Bayes classifier python sklearn
I am trying to get the most important features for my GaussianNB model. The codes from here How to get most informative features for scikit-learn classifiers?
or here How to get most informative features for scikit-learn classifier for different class? only work when I use MultinomialNB. How can I calculate or retrieve the most important features for each of my two classes (Fault = 1 or Fault = 0) otherwise?
My code is: (not applied to text data)
df = df.toPandas()
X = X_df.values
Y = df['FAULT'].values.reshape(-1,1)
gnb = GaussianNB()
y_pred = gnb.fit(X, Y).predict(X)
print(confusion_matrix(Y, y_pred))
print(accuracy_score(Y, y_pred))
Where X_df is a dataframe with binary columns for each of my features.
python scikit-learn classification feature-selection naivebayes
asked Nov 27 '18 at 18:53
LN_PLN_P
14712
add a comment |
I am trying to get the most important features for my GaussianNB model. The codes from here How to get most informative features for scikit-learn classifiers?
or here How to get most informative features for scikit-learn classifier for different class? only work when I use MultinomialNB. How can I calculate or retrieve the most important features for each of my two classes (Fault = 1 or Fault = 0) otherwise?
My code is: (not applied to text data)
df = df.toPandas()
X = X_df.values
Y = df['FAULT'].values.reshape(-1,1)
gnb = GaussianNB()
y_pred = gnb.fit(X, Y).predict(X)
print(confusion_matrix(Y, y_pred))
print(accuracy_score(Y, y_pred))
Where X_df is a dataframe with binary columns for each of my features.
python scikit-learn classification feature-selection naivebayes
asked Nov 27 '18 at 18:53
LN_PLN_P
14712
This accepted answer discusses getting features for only the binary classification case
– G. Anderson
Nov 27 '18 at 19:19
That's the example I cited: it only works for Bernoulli or Multinomial but not Gaussian
– LN_P
Nov 28 '18 at 9:44
add a comment |
I am trying to get the most important features for my GaussianNB model. The codes from here How to get most informative features for scikit-learn classifiers?
or here How to get most informative features for scikit-learn classifier for different class? only work when I use MultinomialNB. How can I calculate or retrieve the most important features for each of my two classes (Fault = 1 or Fault = 0) otherwise?
My code is: (not applied to text data)
df = df.toPandas()
X = X_df.values
Y = df['FAULT'].values.reshape(-1,1)
gnb = GaussianNB()
y_pred = gnb.fit(X, Y).predict(X)
print(confusion_matrix(Y, y_pred))
print(accuracy_score(Y, y_pred))
Where X_df is a dataframe with binary columns for each of my features.
python scikit-learn classification feature-selection naivebayes
asked Nov 27 '18 at 18:53
LN_PLN_P
14712
I am trying to get the most important features for my GaussianNB model. The codes from here How to get most informative features for scikit-learn classifiers?
or here How to get most informative features for scikit-learn classifier for different class? only work when I use MultinomialNB. How can I calculate or retrieve the most important features for each of my two classes (Fault = 1 or Fault = 0) otherwise?
My code is: (not applied to text data)
df = df.toPandas()
X = X_df.values
Y = df['FAULT'].values.reshape(-1,1)
gnb = GaussianNB()
y_pred = gnb.fit(X, Y).predict(X)
print(confusion_matrix(Y, y_pred))
print(accuracy_score(Y, y_pred))
Where X_df is a dataframe with binary columns for each of my features.
python scikit-learn classification feature-selection naivebayes
python scikit-learn classification feature-selection naivebayes
asked Nov 27 '18 at 18:53
LN_PLN_P
14712
asked Nov 27 '18 at 18:53
LN_PLN_P
14712
asked Nov 27 '18 at 18:53
LN_PLN_P
14712
asked Nov 27 '18 at 18:53
LN_PLN_P
14712
asked Nov 27 '18 at 18:53
LN_PLN_P
14712
14712
This accepted answer discusses getting features for only the binary classification case
– G. Anderson
Nov 27 '18 at 19:19
That's the example I cited: it only works for Bernoulli or Multinomial but not Gaussian
– LN_P
Nov 28 '18 at 9:44
add a comment |
This accepted answer discusses getting features for only the binary classification case
– G. Anderson
Nov 27 '18 at 19:19
That's the example I cited: it only works for Bernoulli or Multinomial but not Gaussian
– LN_P
Nov 28 '18 at 9:44
This accepted answer discusses getting features for only the binary classification case
– G. Anderson
Nov 27 '18 at 19:19
This accepted answer discusses getting features for only the binary classification case
– G. Anderson
Nov 27 '18 at 19:19
That's the example I cited: it only works for Bernoulli or Multinomial but not Gaussian
– LN_P
Nov 28 '18 at 9:44
That's the example I cited: it only works for Bernoulli or Multinomial but not Gaussian
– LN_P
Nov 28 '18 at 9:44
add a comment |
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This accepted answer discusses getting features for only the binary classification case
– G. Anderson
Nov 27 '18 at 19:19
That's the example I cited: it only works for Bernoulli or Multinomial but not Gaussian
– LN_P
Nov 28 '18 at 9:44