Classification 2 - Classification Models

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Classification Type 1

Decision Trees

- CART (Classification and Regression Trees)

- C4.5

- C5.0

Ensemble Methods

- Random Forests

- Gradient Boosting Machines (GBM)

- AdaBoost (Adaptive Boosting)

Support Vector Machines (Maximum Margin Classifiers)

- Linear SVM

- Kernel SVM

Neural Networks and Deep Learning

- Multilayer Perceptrons (MLP)

- Convolutional Neural Networks (CNN)

- Recurrent Neural Networks (RNN)

- Long Short-Term Memory Networks (LSTM)

Probabilistic Models

- Naive Bayes

- Bayesian Networks

- Gaussian Processes

Instance-Based Learning

- K-Nearest Neighbors (KNN)

Classification Type 2

Linear Models for Classification : Predict the class label based on a linear combination of input features.

- Logistic Regression

- Linear Discriminant Analysis (LDA)

- Perceptron

 Probabilistic Generative Models : Learn the joint probability distribution of inputs and labels, allowing for the generation of new data samples.

- Naive Bayes

- Gaussian Mixture Models

 Probabilistic Discriminative Models : Directly model the conditional probability of class labels given the input features, focusing on the boundary between classes.

- Logistic Regression

- Probit Regression    


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