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What is the difference between softmax and logistic regression?
Softmax Regression is a generalization of Logistic Regression that summarizes a ‘k’ dimensional vector of arbitrary values to a ‘k’ dimensional vector of values bounded in the range (0, 1). In Logistic Regression we assume that the labels are binary (0 or 1). However, Softmax Regression allows one to handle classes.
Does logistic regression use softmax?
Softmax Regression (synonyms: Multinomial Logistic, Maximum Entropy Classifier, or just Multi-class Logistic Regression) is a generalization of logistic regression that we can use for multi-class classification (under the assumption that the classes are mutually exclusive).
What’s the difference between Softmax and logistic regression?
The first one) is binary classification using logistic regression, the second one is multi-classification using logistic regression with one-vs-all trick and the last one) is mutli-classification using softmax regression. 1. Problem setting Classification problem is to classify different objects into different categories.
How to predict new sample using logistic regression?
Specifically we divide the 2-D plane into 2 parts according to a line, and then we can predict new sample by observing which part it belongs to. Mathematically if z = w0 + w1x1 + w2x2 >= 0, then y = 1; if z = w0 + w1x1 + w2x2 < 0, then y = 0. We can regard the linear function wTx as a mapping from raw sample data ( x1, x2) to classes scores.
How is classification problem similar to regression problem?
Classification problem is to classify different objects into different categories. It is like regression problem, except that the predictor y just has a small number of discrete values. For simplicity, we just focus on binary classification that y can take two values 1 or 0 (indicating two classes). 2. Basic idea
When does the loss need to be small in logistic regression?
When target y = 1, the loss had better be very large when h(x) = 1 1 + e − wTx is close to zero, and the loss should be very small when h (x) is close to one; in the same way, when target y = 0, the loss had better be very small when h (x) is close to zero, and the loss should be very large when h (x) is close to one.