AI-ordlista
Den kompletta ordlistan över AI
Binary Classification
Prediction of a target variable with two possible classes, used for yes/no or true/false type problems.
Multi-class Classification
Extension of binary classification where the model must predict among three or more mutually exclusive classes.
Linear Regression
Statistical model that establishes a linear relationship between input variables and a continuous target variable.
Polynomial Regression
Form of regression that models the nonlinear relationship between variables using polynomial terms.
Decision Trees
Predictive model using a tree-like structure to represent decisions and their possible consequences.
Random Forests
Ensemble method combining multiple decision trees to improve accuracy and control overfitting.
Support Vector Machines (SVM)
Classification algorithm that finds the optimal hyperplane separating classes in a high-dimensional space.
Supervised Neural Networks
Deep learning architectures trained with labeled data to learn hierarchical representations.
k-Nearest Neighbors (k-NN)
Simple algorithm that classifies new observations based on the majority class of its k nearest neighbors.
Naive Bayes
Probabilistic classifier based on Bayes' theorem with an assumption of independence between features.
Gradient Boosting
Ensemble technique that builds models sequentially by correcting the errors of previous models.
Logistic Regression
Regression model used to predict probabilities for binary classification outcomes.
Regularization Methods
Techniques (L1, L2, Elastic Net) to prevent overfitting by penalizing complex model coefficients.
Cross Validation
Robust evaluation technique using multiple partitions of data to estimate model performance.
Supervised Feature Engineering
Process of creating and selecting optimal features specifically for supervised models.