AdaBoost algorithm

The AdaBoost (Adaptive Boosting) algorithm with scikit-learn in Python

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.

Tidyverse and descriptive statistics

Tidyverse, an ideal tool for Descriptive Statistics with R

Descriptive statistics is a crucial step in data analysis, providing a detailed overview of the main characteristics of a dataset. R, with its vast ecosystem of packages, offers a powerful and coherent solution to address this phase. Among these, Tidyverse stands out, a set of packages designed to improve data manipulation, analysis and visualization in R.

Single Layer Perceptron SLP

Let’s build a Single Layer Perceptron (SLP) with Python

This article aims to explore the world of perceptrons, focusing in particular on the Single Layer Perceptron (SLP), which, although it constitutes only a small fraction of the overall architecture of deep neural networks, provides a solid basis for understanding the fundamental mechanisms of Deep Learning. We will also introduce practical implementation examples in Python, illustrating how to build and visualize an SLP using libraries such as NumPy, NetworkX and Matplotlib.

Logistic-Regression

Logistic Regression with Python

Logistic regression is a type of regression model used for binary classification problems, where the goal is to predict which of two classes an instance belongs to. Unlike linear regression, which predicts continuous values, logistic regression predicts probabilities that vary between 0 and 1. This is achieved by using a logistic (or sigmoid) function to transform the linear output into probabilities.