I'm trying to perform feature selection by evaluating my regressions coefficient outputs, and select the features with the highest magnitude coefficients. The problem is, I don't know how to get the respective features, as only coefficients are returned form the coef._ attribute. The documentation says:

Estimated coefficients for the linear regression problem. If multiple targets are passed during the fit (y 2D), this is a 2D array of shape (n_targets, n_features), while if only one target is passed, this is a 1D array of length n_features.

I am passing into my regression.fit(A,B), where A is a 2-D array, with tfidf value for each feature in a document. Example format:

```         "feature1"   "feature2"
"Doc1"    .44          .22
"Doc2"    .11          .6
"Doc3"    .22          .2
```

B are my target values for the data, which are just numbers 1-100 associated with each document:

```"Doc1"    50
"Doc2"    11
"Doc3"    99
```

Using regression.coef_, I get a list of coefficients, but not their corresponding features! How can I get the features? I'm guessing I need to modfy the structure of my B targets, but I don't know how Mar 15 38 views

## 1 answer to this question.

What I discovered to be effective was .Your independent variables are denoted by the letterX.

`coef= pd.concat([pd.DataFrame(X.columns),pd.DataFrame(np.transpose(logistic.coef_))], axis = 1)`

the supposition you made that the order ofregression.coef, is the same as in the TRAIN set is correct. ( workshop with the underpinning data and looks forX-y correlations) answered Mar 17 by
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