1. Introducing regularization to the model always results in equal or better performance on the training set. underfit 될수 있다. -> 결과가 안좋아진다. 2 .Adding many new features to the model helps prevent overfitting on the training set. overfitting 될 수 있다. 3. Introducing regularization to the model always results in equal or better performance on examples not in the training set. regularization 이 과다하면 트레..

P(y = 0 | x; θ) = 1 - P(y = 1| x; θ) = 1 - 0.4 = 0.6 hθ(x) = 0.4 P(y = 0 | x; θ) = 1 - P(y = 1| x; θ) = 1 - 0.2 = 0.8 hθ(x) = 0.2 g(θ0 + θ1x1 + θ2x2) = θ = [6; 0; -1;] 6-x2 >= 0 6 >= x2 x2 가 6보다 작을때 y=1