logistic regression function explained code example

Example 1: logistic regression sklearn

#Logistic Regression Model

from sklearn.linear_model import LogisticRegression
LR = LogisticRegression(random_state=0).fit(X, y)
LR.predict(X[:2, :]) #Return the predictions
LR.score(X, y) #Return the mean accuracy on the given test data and labels

#Regression Metrics
#Mean Absolute Error

from sklearn.metrics import mean_absolute_error 
mean_absolute_error(y_true, y_pred)

#Mean Squared Error

from sklearn.metrics import mean_squared_error
mean_squared_error(y_true, p_pred)

#R2 Score

from sklearn.metrics import r2_score
r2_score(y_true, y_pred)

Example 2: 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)