问题
Question : Logistic Regression Train logistic regression models with L1 regularization and L2 regularization using alpha = 0.1 and lambda = 0.1. Report accuracy, precision, recall, f1-score and print the confusion matrix
My code is :
_lambda = 0.1
c = 1/_lambda
classifier = LogisticRegression(penalty='l1',C=c)
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
I don't know where is really location of alpha and lambda. Did I work right?
回答1:
your example
alpha=0, lambda=10 (AKA .1/1)
alpha
alpha is the parameter that adds penalty for number of features to control overfitting, in this case either L1 (Lasso Regression) or L2 (Ridge Regression). L1 and L2 penalty cannot both be done at the same time, as there is only one Lambda coefficient. Quick aside - Elastic Net is an alpha parameter that is somewhere in between L1 and L2, so for example, if you are using sklearn.SGD_Regressor() alpha=0 is L1 alpha=0.5 is elasticnet, alpha=1 is Ridge.
Lambda
is a term that controls the learning rate. In other words, how much change do you want the model to make during each iteration of learning.
confusion
To make matters worse, these terms are often used interchangedly, I think due to different yet similar concepts in graph theory, statistical theory, mathematical theory, and the individuals who write commonly-used machine-learning libraries
check out some info here: https://www.analyticsvidhya.com/blog/2016/01/complete-tutorial-ridge-lasso-regression-python/ but also look for some of the free academic textbooks about statistical learning.
来源：https://stackoverflow.com/questions/52097663/problem-with-alpha-and-lambda-regularization-parameters-in-python