What is Classification?
Assigning data into categorical compartments (ex: cat/dog, apple/pear/banana, etc.) is Classification.
Assigning data into categorical compartments (ex: cat/dog, apple/pear/banana, etc.) is Classification.
Most of the concepts of binary classification transfer over to the situation of an outcome with more than two levels, which is referred to as multi-class classification.
Logistic regression is the most traditional classification algorithm and preserves many of the advantages in interpretation as linear regression for a continuous outcome.
If the overall distribution of the outcome is heavily tilted towards one class compared to the other, the classification problem is considered to be imbalanced.
One of the decisions that often has to be made in a classification problem is determining what threshold to use in order to classify observations into appropriate class designations.
Both discriminative and generative models have the ability to learn model parameters in a classification setting, but they are based on entirely different mechanisms and serve different purposes.
Logistic regression is a supervised learning algorithm that is used to predict the probability of categorical dependent variable.
Logistic regression uses a logistic loss function, where the cost for a single observation is represented by:
SVM is a classification algorithm that seeks to determine a decision boundary by maximizing the distance between points of different classes.
Naive Bayes uses the framework of Bayes Theorem and the assumption of conditional independence between all pairs of predictors
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