Logistic Regression
Binary logistic regression
Converges to minimum loss but will just draw linearly boundaries on data
Can only handle 2 classes
Main characteristics
Binary Classification Model
Probabilistic output (0. - 1.)
Cannot use non-linear decision boundaries
Uses Sigmoid or Logistic function as the activation function
Threshold function used to pick a discrete binary output
Relevant properties:
Assumes:
linearity between features and log-odds
independent observations
minimal multicollinearity
Supports Lasso (L1) and Ridge (L2) regularization to prevent overfitting.
Weight coefficients are interpretable

Multiclass Logistic Regression
Also known as Softmax Regression Model or Multinomial Logistic Regression:
Softmax activation, instead of Sigmoid or Logistic function
One-hot Encoded labels, instead of 0/1 labels
Multiclass Cross Entropy, instead of Binary cross-entropy as the loss function

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