Discover published sets by community

Linear Regression is a statistical approach for modeling the relationship between a dependent variable and one or more independent variables using a linear equation. It is commonly used for predicting numerical values in data science.

Decision Trees are a non-linear model used for both classification and regression. They split the data into subsets based on certain criterion, creating a tree-like model of decisions.

Logistic Regression is used for binary classification tasks, where the outcome is discrete. The algorithm predicts the probability that the given input belongs to a certain class.

Random Forest is an ensemble learning method that operates by constructing multiple decision trees at training time and outputting the class that is the mode of the classes from individual trees.

KNN is a simple, instance-based learning algorithm that classifies a new case based on the majority vote of its 'k' nearest neighbors.

SVM is a powerful classifier that works by finding the hyperplane that best divides a dataset into classes. It is effective in high dimensional spaces.

Naive Bayes classifiers are a family of simple probabilistic classifiers based on applying Bayes' theorem with strong independence assumptions between the features.