logistic regression using sklearn 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: LogisticRegression sklearn

from sklearn.linear_model import LogisticRegression

Example 3: multinomial regression scikit learn

model1 = LogisticRegression(random_state=0, multi_class='multinomial', penalty='none', solver='newton-cg').fit(X_train, y_train)
preds = model1.predict(X_test)

#print the tunable parameters (They were not tuned in this example, everything kept as default)
params = model1.get_params()
print(params)

{'C': 1.0, 'class_weight': None, 'dual': False, 'fit_intercept': True, 'intercept_scaling': 1, 'l1_ratio': None, 'max_iter': 100, 'multi_class': 'multinomial', 'n_jobs': None, 'penalty': 'none', 'random_state': 0, 'solver': 'newton-cg', 'tol': 0.0001, 'verbose': 0, 'warm_start': False}

Example 4: importing logistic regression

sklearn.linear_model.LogisticRegression