Regularized Logistic Regression February 26, 2017 Get link Facebook X Pinterest Email Other Apps Regularized Logistic Regression J(θ)=−1m∑mi=1[y(i) log(hθ(x(i)))+(1−y(i)) log(1−hθ(x(i)))]+λ2m∑nj=1θ2j Note: the 2nd sum explicitly skip j = 0 because we treat that separately as below Resources: - https://www.coursera.org/learn/machine-learning/supplement/v51eg/regularized-logistic-regression Comments
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