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Mean Shift is an unsupervised clustering algorithm that aims to discover blobs in a smooth density of samples. It is a centroid-based algorithm that works by updating candidates for centroids to be the mean of the points within a given region (also called bandwidth).

Affinity Propagation is a clustering algorithm that is commonly used in Machine Learning and data analysis. Unlike other traditional clustering algorithms which require specifying the number of clusters beforehand, Affinity Propagation discovers cluster centres and assigns data points to clusters autonomously.

OPTICS ('Ordering Points To Identify Clustering Structure') is an augmented ordering algorithm which means that instead of assigning cluster memberships, it stores the order in which the points are processed.

DBSCAN - Density-Based Spatial Clustering of Applications with Noise. Finds core samples of high density and expands clusters from them. Good for data which contains clusters of similar density.

Boosted Decision Tree Regression - 1000 estimators for AdaBoost Regressor on 10000 traning samples

Boosted Decision Tree Regression - 30 estimators for AdaBoost Regressor on 10000 traning samples

Boosted Decision Tree Regression - 500 estimators for AdaBoost Regressor on 5000 traning samples

Boosted Decision Tree Regression - 20 estimators for AdaBoost Regressor on 5000 traning samples

Boosted Decision Tree Regression - 10000 estimators for AdaBoost Regressor on 1000 traning samples

Boosted

Decision tree is used to predict simultaneously the noisy x and y observations of a circle given a single underlying feature. As a result, it learns local linear regressions approximating the circle.

Decision Tree is used to fit a sine curve with addition noisy observation. As a result, it learns local linear regressions approximating the sine curve.

We do compare prediction result of linear regression (linear model) and decision tree (tree based model) with and without discretization of real-valued features.

Polynomial fit of a non-linear feature with 1 million samples

Polynomial fit of a non-linear feature with 500 thousand samples

Polynomial fit of a non-linear feature with 100 thousand samples

Polynomial fit of a non-linear feature with 10 thousand samples

Polynomial fit of a non-linear feature with 3 thousand samples

Polynomial fit of a non-linear feature with 1 thousand samples

Polynomial fit of a non-linear feature with 300 samples

Code

Mobile App + Web App + AI + OpenAI + Machine Lear

Logistic Regression Path with Digits dataset

Logistic Regression Path with Wine dataset

Logistic Regression Path with Breast Cancer dataset

Logistic Regression Path with Iris dataset

Code

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In statistics, least-angle regression (LARS) is an algorithm for fitting linear regression models to high-dimensional data.

In essence, the LASSO solution path gives us a continuum of models to choose from. One can see a gradient of models, and use various "goodness of fit" metrics, or an ensemble of multiple metrics, to choose a point on that solution path.