Popular machine learning models
2 min readFeb 11, 2023
- K-Nearest Neighbors (KNN): This is a simple supervised learning algorithm used for classification and regression. The idea is to find the “K” nearest data points to a given test data point and use the labels (or values) of those nearest data points to make a prediction.
- Naive Bayes: This is a simple probabilistic algorithm used for classification. It is based on Bayes’ theorem, which states that the probability of a class given the features is proportional to the probability of the features given the class.
- Random Forest: This is an ensemble method for both regression and classification, where multiple decision trees are combined to produce a more accurate prediction. The idea is to train many decision trees on random subsets of the data and average (for regression) or vote (for classification) their predictions.
- Support Vector Machines (SVM): This is a powerful supervised learning algorithm used for classification and regression. The idea is to find the maximum margin hyperplane that separates the data into different classes or to find the optimal hyperplane that fits the data in regression.
- Gradient Boosting: This is an ensemble method for both regression and classification, where weak decision trees are combined to produce a more accurate prediction. The idea is to train many simple decision trees one at a time and use the errors from the previous trees to weight the training of the next trees.
- Convolutional Neural Networks (CNNs): This is a type of neural network specifically designed for image classification and object recognition. The idea is to use convolutional layers to learn local features in the image and use pooling layers to reduce the spatial dimensions and increase the abstraction level of the features.
- Recurrent Neural Networks (RNNs): This is a type of neural network specifically designed for sequential data, such as time series, text, and speech. The idea is to use recurrent connections to capture the dependencies between the elements in the sequence and use hidden states to represent the memory of the network.
