# Classification AI Algorithms

There are many Classification AI algorithms. Some popular ones are:

### Naïve Bayesian

classification is a supervised machine learning algorithm used for classification tasks
by modeling input class distribution on the assumption that
the presence of a particular feature in a class is independent of the presence of any other feature.
Its (over)simplicity only requires a linear number of parameters to the number of variables in a learning process -
hence only takes linear learning time.

### Decision Trees

are a type of supervised learning algorithm for both classification and regression tasks.
They work by recursively partitioning the input space into smaller regions based on the values of the input features.

### Support Vector Machines (SVM)

is a supervised learning algorithm to define the hyperplane
that best separates the input data into different classes for classification and regression tasks.

### Logistic Regression

is a statistical method that can be used for binary (YES or NO) classification tasks.

### K-Nearest Neighbors (KNN)

classification is a non-parametric algorithm that finds the
k-nearest neighbors of the input data in the feature space and uses their labels to derive the label for classification and regression tasks.

### Random Forests

classification combines multiple decision trees to improve the accuracy and robustness of the classification model.

Classification models may achieve higher accuracy levels when coupled with

### Kernel Density Estimation (KDE)

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KDE is is a mathematical process used in machine learning to estimate the probability density function of a random variable as a smooth curve.
The curve is calculated by weighing the distance of all the points in each specific location along the distribution.
If there are more points grouped locally, the probability of seeing a point at that location is higher.
The kernel function is the mechanism to weigh the points across the data set.
It is used in spatial analysis to create heat maps and also used in machine learning for tasks such as density estimation, clustering, and anomaly detection.

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