Machine Learning

Handwriting Digit Recognition Handwriting digit recognition typically involves using machine learning or deep learning models to classify handwritten digits, such as those found in the popular MNIST dataset. Here’s an overview of how it’s usually done: 1. Data Collection¶ MNIST Dataset: The most common dataset for digit recognition, containing 60,000 …

PCA – ITRONIX SOLUTIONS Principle Component Analysis (PCA) with Scikit-Learn PCA Steps Standardize the data. Use the standardized data to create a covariance matrix. Use the resulting matrix to calculate eigenvectors (principal components) and their corresponding eigenvalues. Sort the components in decending order by its eigenvalue. Choose n components which …

LDA 1. What is Dimensionality Reduction?¶In Machine Learning and Statistic, Dimensionality Reduction the process of reducing the number of random variables under consideration via obtaining a set of principal variables. It can be divided into feature selection and feature extraction. We will deal with two main algorithms in Dimensionality Reduction¶ …

PCA – introduction What is PCA (Principal Component Analysis)?¶Principal Component Analysis (PCA) is an unsupervised dimensionality reduction technique commonly used in data science and machine learning. Its primary goal is to reduce the number of features (dimensions) in a dataset while preserving as much variability (information) as possible. Key Concepts …

LDA – introduction What is LDA (Linear Discriminant Analysis)?¶Linear Discriminant Analysis (LDA) is a classification and dimensionality reduction technique commonly used in machine learning and statistics. Its main goal is to project high-dimensional data onto a lower-dimensional space while maximizing the separability between different classes. Key Concepts of LDA¶ Supervised …

1 – Grid Search using K-Fold What is Grid Search?¶ Think of a Recipe: Imagine you want to make a perfect cake. You have different ingredients (like flour, sugar, eggs) and different amounts for each ingredient. Grid search is like trying out all possible combinations of these ingredients to find …

Grid Search What is Grid Search?¶Grid Search is a hyperparameter tuning technique used in machine learning to optimize model performance by systematically searching through a specified set of hyperparameter values. Hyperparameters are settings that govern the training process of a model, and finding the right combination can significantly impact the …

Grid Search using K-Fold 2022 Grid Search using K-Fold Cross-Validation is a technique that combines hyperparameter tuning with robust model evaluation. This method helps find the optimal hyperparameters for a machine learning model while ensuring that the evaluation of those hyperparameters is reliable and not dependent on a specific train-test …

grid search k fold cross validation Grid Search with K-Fold Cross-Validation is a technique used in machine learning to help you find the best settings (hyperparameters) for your model. Here’s a simple breakdown: 1. Grid Search¶ What it is: Imagine you want to bake a cake, but you’re unsure about …

XGBoost 2022 What is XGBoost?¶ XGBoost (Extreme Gradient Boosting) is a machine learning algorithm that implements the gradient boosting framework. It is designed for efficiency, flexibility, and performance, making it suitable for structured or tabular data. Key Concepts¶ Boosting: A technique where models are built sequentially, with each new model …