#scikitlearn

Clustering algorithms are an unsupervised machine learning technique used to group similar data points without using labels. Its purpose is to locate relationships or patterns without prior knowledge.

Unsupervised machine learning is when the model groups similar data points on its own without using labeled data.

Hard clustering. Hard cluster assigns each data point to exactly one cluster.

Centroid based Clustering. Centroid based cluster groups data points around central points called centroids. Each cluster is represented by the average of its points and the data points are assigned to the nearest centroid.

Density based Clustering. Density based clustering identifies clusters as regions where data points are densely packed together. Points in low density areas are treated as noise.

K-means. Groups data into K clusters based on how close the data is to it each other. Hard Clustering. Unsupervised technique. Centroid based clustering.

Density Based Clustering (DBscan). Finds clusters in dense groups and ignores scattered points as noise. The first cluster gets the non-core points near both the first and second clusters. Hard Clustering. Unsupervised technique. Density based Clustering.

All the dots are plant measurements in this exercise.

Q. What is a Clustering algorithm? Why are they used? Advantages and disadvantages? Give an example?

Q. What is Kmeans? Is it a hard or soft clustering algorithm?

Q. What is a Centroid based clustering algorithm? Give an example and explain how it works.

Q. What is a Density based clustering algorithm? Give an example and explain how it works.

Q. Explain how DBspan works.

Q. If I have a noisy dataset with irregular clusters, should I use DBscan or K-means?

Q. If I have a large dataset with spherical clusters and know the number of clusters, which algorithm should I use?

Cross-validation stands as a cornerstone of robust machine learning model development, enabling us to assess how effectively a model generalizes to data it hasn’t encountered during training. It’s significantly more reliable than a single train/test split because repeated resampling offers a broader performance evaluation. Understanding and implementing various cross-validation techniques is…

Project Title: cddml-MvTyBNKrPxS – “Hierarchical Ensemble Stacking for Wine Classification with Custom Feature Engineering” File Name: hierarchical_ensemble_stacking_for_wine_classification.py Below is a very advanced project that leverages scikit-learn’s pipelines, custom transformers, and a stacking ensemble for multi-class classification using the UCI Wine dataset. The focus is on creating…

Project Title: cddml-MvTyBNKrPxS – “Hierarchical Ensemble Stacking for Wine Classification with Custom Feature Engineering” File Name: hierarchical_ensemble_stacking_for_wine_classification.py Below is a very advanced project that leverages scikit-learn’s pipelines, custom transformers, and a stacking ensemble for multi-class classification using the UCI Wine dataset. The focus is on creating…

Project Title: cddml-MvTyBNKrPxS – “Hierarchical Ensemble Stacking for Wine Classification with Custom Feature Engineering” File Name: hierarchical_ensemble_stacking_for_wine_classification.py Below is a very advanced project that leverages scikit-learn’s pipelines, custom transformers, and a stacking ensemble for multi-class classification using the UCI Wine dataset. The focus is on creating…