33 templates include:
- Linear Regression: A simple algorithm for predicting a continuous outcome based on linear relationships.
- Logistic Regression: Used for binary classification problems, estimating probabilities using a logistic function.
- k-Nearest Neighbors (k-NN): Classifies a data point based on the majority class of its k-nearest neighbors.
- Naive Bayes: A probabilistic algorithm based on Bayes’ theorem, often used for classification.
- Decision Trees: Hierarchical tree-like structures for decision-making, widely used in classification and regression.
- Random Forest: An ensemble of decision trees, providing robustness and improved performance.
- Support Vector Machines (SVM): Classifies data points by finding the hyperplane that maximizes the margin between classes.
- Principal Component Analysis (PCA): Reduces dimensionality by identifying the most important features in the data.
- K-Means Clustering: Divides data into k clusters based on similarity.
- Hierarchical Clustering: Builds a hierarchy of clusters by merging or splitting them iteratively.
- Gradient Descent: An optimization algorithm used for finding the…