RFA package for implementing random forest adjustment.
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Updated
Sep 20, 2023 - R
RFA package for implementing random forest adjustment.
Data Science - Case Study with Classification Application in Python Using scikit-learn
Price Prediction using Random Forests
Develop a Lead Prediction System to enhance marketing efforts by accurately identifying prospective customers.
Random Forest Classifier
Used Supervised Classification Predictive Machine Learning models such as Decision Trees, KNN, Logistic Regression, Random Forests, and SVM
A 20m presentation showing the concepts behind oblique random survival forest and some of its recent applications.
Material for the Computational Statistics Project | Summer 2022 | University of Bonn
Poetry Identification Code from my dissertation runs on zip files containing DJVUXML from the Internet Archive.
Exploratory data analysis and predicting diabetics using PySpark
Supervised Machine Learning using SciKit and other tools to do PCA, SVM, random forests, etc. for facial recognition and predictive decision making.
Data Analytics and Machine Learning in R. Linear-regression, Logistic-regression, Hierarchical-clustering, Boosting, Bagging, Random-forests, K-means-clustering, K-nearest-neighbors (K-N-N), Tree-pruning, Subset-selection, LDA, QDA, Support Vector Machines (SVM)
This repo contains material for a workshop on Random Forests in phonetics/phonology research
Embankment dam land-cover segmentation based on multispectral remote sensing imagery.
Predicts if a driver is fit to drive or not. Performance of Logistic Regression, Naive Bayes, and Random Forests using Scikit-Learn is compared.
The ML-GYM repository showcases machine learning projects using **scikit-learn**, covering classification, regression, and clustering. It offers educational resources for beginners and practical examples for experienced users, complete with detailed instructions.
Decision_Trees_and_Random_Forests
Fast explication of different models based in trees
Handwritten Digit Recognition with Random Forests algorithm
This was a binary classification task in which I had to determine if and article got at least 1400 shares. I wanted to use few different machine learning algorithms to compare their accuracy on that data. I chose to use: Decision Tree, Random Forests and Multi Layer Perceptron.
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