Machine Learning Algorithms
Machine learning algorithms that train live in your browser.
Regression
Linear regression
Fits the straight line that minimises the squared distance between predictions and the data.
Polynomial regression
Adds powers of the input so a linear model can fit curves.
Ridge & lasso regression
A flexible polynomial kept in check by a penalty on large coefficients.
k-nearest neighbours regression
Predicts by averaging the targets of the k closest training points.
Decision tree regression
Splits the input into intervals and predicts the average target in each.
Classification
Logistic regression
Models the probability of a class with a weighted sum passed through a sigmoid.
k-nearest neighbours
Classifies a point by the majority class among its k nearest neighbours.
Decision tree
Learns a sequence of yes/no questions on the features that ends in a class.
Naive Bayes (Gaussian)
Applies Bayes’ theorem, assuming each feature is normally distributed and independent within a class.
Support vector machine
Finds the boundary with the widest margin between the classes, optionally in a kernel-transformed space.
Neural network (MLP)
Layers of simple neurons trained by gradient descent, able to learn curved boundaries.
Clustering
k-means
Splits points into k groups by alternately assigning points to the nearest centre and moving centres to their mean.
DBSCAN
Grows clusters from dense regions and labels isolated points as noise.
Hierarchical clustering
Merges the closest clusters step by step, building a tree you can cut at any level.