PYTHON

logistic regression algorithm in python

# import the class
from sklearn.linear_model import LogisticRegression

# instantiate the model (using the default parameters)
logreg = LogisticRegression()

# fit the model with data
logreg.fit(X_train,y_train)

#
y_pred=logreg.predict(X_test)

logistic regression algorithm in python

print("Accuracy:",metrics.accuracy_score(y_test, y_pred))
print("Precision:",metrics.precision_score(y_test, y_pred))
print("Recall:",metrics.recall_score(y_test, y_pred))

logistic regression algorithm in python

# import the metrics class
from sklearn import metrics
cnf_matrix = metrics.confusion_matrix(y_test, y_pred)
cnf_matrix

importing logistic regression

sklearn.linear_model.LogisticRegression

logistic regression sklearn

LogisticRegression(penalty='l2',C=1.0, fit_intercept=True, max_iter=100)