9 docs tagged with "data visualization"

Implement the AdaBoost algorithm to combine multiple weak classifiers into a strong ensemble model. This feature will visualize the boosting process and support various base learners.

Explore the Gradient Boosting Machines (GBM) algorithm for machine learning, including popular variants like XGBoost and LightGBM. Learn how it builds models sequentially, improving performance by correcting errors from previous models.

Implement hierarchical clustering algorithms that build a hierarchy of clusters using either agglomerative or divisive methods. This feature will include visualizations to help users understand the clustering process.

Implement the K-Means clustering algorithm to partition data into K clusters based on feature similarity. This feature will include visualizations to help users understand the clustering process.

Build and visualize neural networks with support for feedforward, convolutional, and recurrent architectures. Explore how these models learn from data using backpropagation and gradient descent.

Implement Principal Component Analysis (PCA) to reduce the dimensionality of high-dimensional data while preserving its essential features. Visualize the transformed data to gain insights into underlying patterns.

This post explores Support Vector Machines (SVM), a powerful classification algorithm that finds the optimal hyperplane to separate different classes in high-dimensional datasets.

An overview of t-SNE, a popular technique for visualizing high-dimensional data in two or three dimensions.

This post explores t-SNE (t-distributed Stochastic Neighbor Embedding), a popular dimensionality reduction technique used to visualize high-dimensional data in a low-dimensional space.