21 Key Techniques and Algorithms in Predictive Modeling

Photo by Markus Blüthner on Unsplash

Predictive modeling encompasses a wide range of techniques and algorithms used to make predictions or forecasts based on data. Here is a list of some common predictive modeling techniques:

1. Linear Regression: Linear regression is used to model the relationship between a dependent variable and one or more independent variables using a linear equation.
2. Logistic Regression: Logistic regression is employed for binary classification problems, where the outcome is a binary variable (e.g., yes/no, 0/1).
3. Decision Trees: Decision trees are tree-like structures used for both classification and regression tasks, making decisions based on input features.
4. Random Forest: Random forests are ensemble learning methods that combine multiple decision trees to improve predictive accuracy.
5. Gradient Boosting: Gradient boosting algorithms, such as XGBoost and LightGBM, build an ensemble of decision trees sequentially to optimize predictive performance.
6. Support Vector Machines (SVM): SVMs are used for classification tasks, aiming to find the hyperplane that best separates data points of different classes.
7. K-Nearest Neighbors (K-NN): K-NN classifies data points based on the majority class among their k-nearest neighbors.