#tabular-ml

A Decision Tree is a supervised learning algorithm used for both classification and regression tasks.

Gradient Boosting is an ensemble machine learning technique (boosting) that combines weak learners (typically shallow Decision Tree) sequentially to create a strong predictive model.

K-means is an unsupervised machine learning algorithm used for partitioning a dataset into K distinct, non-overlapping subgroups (clusters).

Linear regression is a supervised algorithm or statistical method that learns to model a dependent variable (target) as a function of some independent variables (features) by finding a line (or surface) that best “fits” the data.

Logistic regression is a statistical method used for binary classification problems, modeling the probability of an instance belonging to a particular class.

Random Forest is an ensemble learning method (bagging) that constructs a multitude of decision trees at training time and outputs the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees.

Support Vector Machines (SVM) is a supervised learning algorithm used for classification, regression, and outlier detection.

Regression metrics are quantitative measures used to evaluate the performance of regression models, which predict continuous values rather than discrete classes.