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)