# How to interpret my logistic regression result with statsmodels

so I'am doing a logistic regression with statsmodels and sklearn. My result confuses me a bit. I used a feature selection algorithm in my previous step, which tells me to only use feature1 for my regression.

The results are the following:

So the model predicts everything with a 1 and my P-value is < 0.05 which means its a pretty good indicator to me. But the accuracy score is < 0.6 what means it doesn't say anything basically.

Can you give me a hint how to interpret this? It's my first data science project with difficult data.

My code:

X = df_n_4["feat1"]
y = df_n_4['Survival']

# use train/test split with different random_state values
# we can change the random_state values that changes the accuracy scores
# the scores change a lot, this is why testing scores is a high-variance estimate
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=2)
print(len(y_train)," Testdaten")

# check classification scores of logistic regression
logit_model = sm.Logit(y_train, X_train).fit()
y_pred = logit_model.predict(X_test)
print('Train/Test split results:')
plt.title('Accuracy Score:{}, Variablen: feat1'.format(round((accuracy_score(y_test, y_pred.round())),3)))
cf_matrix = confusion_matrix(y_test, y_pred.round())
sns.heatmap(cf_matrix, annot=True)
plt.ylabel('Actual Szenario');
plt.xlabel('Predicted Szenario');
plt.show()
print(logit_model.summary2())


Something's wrong with your feature selection tool: p-value is NaN, confidence interval includes $$0$$. Confusion matrix shows that all observations are predicted as Class 1. How many explanatory variables do you have? Try using all of them instead of just one. Are you sure
logit_model = sm.Logit(y_train, X_train).fit()

is correct? Shouldn't it be the other way around, logit_model = sm.Logit(X_train, y_train).fit()?
• I think it's correctly like logit_model = sm.Logit(y_train, X_train).fit(). What do you mean with your confidence interval? In my model where I use all features it works better. But if I use sklearn and one feature it works as well. It's all so confusing! – grumpyp Jan 17 at 13:02