Never in the same shape
Hierarchical clustering is a clustering technique used in machine learning to group similar data sets into larger clusters, organizing them into a hierarchical structure. This clustering method was primarily developed to address unsupervised classification problems, where the data is not pre-labeled and the model must find structure in the data on its own.
DBSCAN, an acronym for Density-Based Spatial Clustering of Applications with Noise, is a clustering algorithm used in machine learning to group data sets based on their density in space. The main goal of DBSCAN is to identify regions of high density separated by regions of low density.
K-means is one of the most popular and widely used clustering algorithms in the field of machine learning and data analytics. It is an unsupervised learning algorithm that aims to partition a dataset into K distinct clusters. The term “K” in K-means indicates the desired number of clusters.
Clustering is an unsupervised machine learning technique used to group similar data sets together to identify patterns or structures in the data space. The goal is to divide a data set into homogeneous groups, so that elements within the same group are more similar to each other than those in different groups.
XGBoost is an open-source library that has gained considerable popularity in the data science community for its effectiveness in solving a wide range of supervised machine learning problems. This library, primarily developed by Tianqi Chen, offers a powerful tree boosting algorithm that relies on successive iterations to improve model accuracy. One of its standout features is the ability to easily handle missing data during the training process, significantly simplifying the workflow for users.
LightGBM is an extremely powerful and versatile machine learning library that has quickly gained popularity in the data science and machine learning community. It began as an open-source project developed by Microsoft and has evolved to become one of the most used libraries for regression, classification and ranking problems.
In the modern data era, machine learning has become an essential component for extracting meaningful insights and data-driven decision making. In this article, we will explore the features and capabilities of the Scikit-learn library, a versatile and powerful tool for machine learning in Python. From data preparation to model building and performance evaluation, Scikit-learn offers a wide range of tools to tackle a variety of machine learning problems.
The AdaBoost algorithm is an ensemble learning technique that combines several weak classifiers to create one strong classifier. Using Python and scikit-learn, we will implement AdaBoost for classification, including a simple example with the Iris dataset. The code will include data loading, splitting into training and test sets, model training, predictions, and performance evaluation. Additionally, we will visualize the results for a deeper understanding.
The Naive Bayes algorithm is a probabilistic classifier based on Bayes’ theorem. It is often used for classification problems, where the goal is to assign a class or category to a set of data based on certain characteristics. The “naive” approach derives from the assumption of conditional independence of characteristics, which simplifies the calculation of probabilities.
The K-Nearest Neighbor algorithm is a supervised learning method used for classification and regression. The main idea is to classify a data point based on the majority of labels of its k nearest instances in the training set. “Closeness” is often measured using Euclidean distance.