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Machine Learning

536 learners

Cluster Performance Unveiled

Explore an in-depth analysis of clustering model validation, delving into techniques that evaluate, refine, and optimize the performance of clustering algorithms. We'll discuss the Silhouette Score, Davis-Bouldin Index, and Cross-Tabulation Analysis, learning how to implement these practices to identify optimal clustering structures.

MatPlotLib

Pandas

Python

Scikit-learn

See path

6 lessons

22 practices

3 hours

Model Validation and Selection

Lessons and practices

Lesson 1: Mastering Cluster Validation with Silhouette Scores and Visualization in Python

Visualizing Clusters and Calculating Silhouette Score

Crafting the Distance Function

Calculating the Average Silhouette Score

Silhouette Score: Write the Code from Scratch

Lesson 2: Mastering the Davies-Bouldin Index for Clustering Model Validation

Stellar Squadron Organization: Calculating the Davies-Bouldin Index

Crafting the Cluster Tightness Function

Calculating the Davies-Bouldin Index for Cluster Analysis

Calculating Cluster Tightness for Davies-Bouldin Index

Lesson 3: Cross-Tabulation Analysis in Clustering: A Python Approach

Exploring Cluster Assignments with Cross-Tabulation

Cross-Tabulation Power Unleashed

Implementing Cross-Tabulation Analysis with Pandas

Lesson 4: Evaluating K-Means Clustering Performance with Python Metrics

Evaluating Clustering Performance on Iris Dataset

Adjusting Cluster Count in KMeans Clustering

Calculating and Evaluating the Davies-Bouldin Index

Cluster Validation Odyssey: From K-means to Metrics

Lesson 5: Assessing Hierarchical Clustering Models with Scikit-learn Metrics

Evaluating Hierarchical Clustering with Silhouette and Davies-Bouldin Scores

Exploring Cluster Quantities in Hierarchical Clustering

Calculating Clustering Effectiveness

Crafting Clusters and Validating Performance

Lesson 6: Evaluating Cluster Analysis in Python: Using DBSCAN and Validity Indices

Unveiling Star Clusters with DBSCAN

Adjusting DBSCAN Parameters

Gauging the Cluster Vastness

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