The top 10 machine learning models in use today are:
1. Linear Regression: A simple model used for predicting a continuous target variable based on linear relationships with input features.
2. Logistic Regression: A model used for classification problems where the target variable is binary, providing probabilities for the target class.
3. Decision Trees: A model that predicts the value of a target variable based on decision rules inferred from the input features.
4. Random Forests: An ensemble model that combines multiple decision trees to make more accurate predictions.
5. Gradient Boosting Machines (GBM): Another ensemble model that builds decision trees sequentially, where each tree tries to correct its predecessor’s mistakes.
6. Support Vector Machines (SVM): A powerful model used for both classification and regression tasks, separating classes using hyperplanes or nonlinear decision boundaries.
7. Neural Networks: A highly flexible model that can learn complex patterns from large amounts of data, inspired by the structure of the brain.
8. K-Nearest Neighbors (KNN): A simple classification model that predicts the class of new instances based on the majority class of its k nearest neighbors.
9. Naive Bayes: A probabilistic model that predicts the probability of classes based on assumptions of strong independence between features.
10. Principal Component Analysis (PCA): A dimensionality reduction technique used to identify important features in high-dimensional data by projecting it onto a lower-dimensional space.
Note that the popularity and usage of these models may vary depending on the specific problem, dataset, and domain.