Logistic Regression
Good
- Less prone to over-fitting but it can overfit in high dimensional datasets.
- Efficient when the dataset has features that are linearly separable.
- Easy to implement and efficient to train.
Bad
- Should not be used when the number of observations are lesser than the number of features.
- Assumption of linearity which is rare in practise.
- Can only be used to predict discrete functions.
What is Classification?
- Classification: A Machine Learning technique to identify the category of new observations based on training data.
Logistic Regression Intuition
- Logistic Regression: Predict a categorical dependent variable from a number of independent variables.
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