Write the logistic regression loss function

Q: Write the logistic regression loss function

This question evaluates understanding of logistic regression loss formulation and L2 regularization, focusing on the binary probabilistic model and the per-example and aggregated objective (binary logistic/cross-entropy) used during training.

Q: How do I approach Statistics & Math interview questions?

Statistics & Math questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master statistics & math interviews.

Question

Logistic Regression Loss

Consider binary logistic regression.

Dataset: $\{(\mathbf{x}_i, y_i)\}_{i=1}^n$
Labels: $y_i \in \{0,1\}$
Model: $p_i = P(y_i=1\mid \mathbf{x}_i) = \sigma(\mathbf{w}^\top \mathbf{x}_i + b)$ , where $\sigma(z)=\frac{1}{1+e^{-z}}$ .

Question

Write the per-example loss.
Write the total loss over $n$ examples (average or sum).
(Optional) Write the L2-regularized objective.

Write the logistic regression loss function

Logistic Regression Loss

Question

Solution

Comments (0)

Write the logistic regression loss function

Overview

Logistic Regression Loss

Question

Solution

Comments (0)