boosting-tree

There are 30 repositories under boosting-tree topic.

  • cerlymarco/linear-tree

    A python library to build Model Trees with Linear Models at the leaves.

    Language:Jupyter Notebook373124356

  • adele-k02/CS229-machine-learning-solar-energy-predictions

    Predicting solar energy using machine learning (LSTM, PCA, boosting). This is our CS 229 project from autumn 2017. Report and poster are included.

    Language:Python602113

  • rosetta-ai/rosetta_recsys2019

    The 4th Place Solution to the 2019 ACM Recsys Challenge by Team RosettaAI

    Language:Python583116

  • yubin-park/bonsai-dt

    Programmable Decision Tree Framework

    Language:Python35646

  • NaoMatch/FortLearner

    Machine Learning Algorithms in Fortran

    Language:Fortran262324

  • kongzii/SwiftXGBoost

    Swift wrapper for XGBoost gradient boosting machine learning framework with Numpy and TensorFlow support.

    Language:Swift25331

  • xiaodaigh/JLBoostMLJ.jl

    MLJ.jl interface for JLBoost.jl

    Language:Julia921

  • DhruvaKumarS/Data-Mining

    CSE601 Course Projects - Fall 2017

    Language:Python3100

  • prs98/Telecom_Churn_Analysis

    This project focuses on segmenting customers based on their tenure, creating "cohorts", allowing us to examine differences between customer cohort segments and determine the best tree based ML model.

    Language:Jupyter Notebook3102

  • karthik-d/FungiCLEF-2022-using-Network-Ensembles

    Scripts, figures and working notes for the participation in FungiCLEF-2022, part of the 13th CLEF Conference, 2022

    Language:Python2103

  • diem-ai/loan-prediction

    Building classification models to predict if a loan application is approved. Using under-sampling, bagging and boosting to tackle the problem of with unbalanced dataset

    Language:Jupyter Notebook110

  • GINK03/bing-ranking-inspector

    Microsoft Bingのランキングの重みを自然言語的に解釈、表現します

    Language:Python1211

  • karthik-d/SnakeCLEF-2022-using-Network-Ensembles

    Scripts, figures and working notes for the participation in SnakeCLEF-2022, part of the 13th CLEF Conference, 2022

    Language:Python1103

  • niketan108/RF-and-GBDT-using-XGBOOST-on-amazon-food-dataset

    Built Random Forest and GBDT using XGBOOST model on Amazon fine food review dataset

    Language:Jupyter Notebook1101

  • unnatibshah/LASSO-and-Boosting-for-Regression

    LASSO and Boosting for Regression on Communities and Crime data

    Language:Jupyter Notebook1100

  • Amoko/Classification-based-on-Hierarchy-Rule-Tree

    gene classification based on partial-order rule mining

    Language:Python0100

  • AshwilNambiar/Datascience

    Datascience hands on code

    Language:Jupyter Notebook0100

  • DanielaRosero/Gradient-Boosting-Classifier-for-Wine-Classification

    Use of Weights & Biases to systematically tune and evaluate the hyperparameters of a Gradient Boosting Classifier. The dataset we are working with is the Wine dataset.

    Language:Jupyter Notebook0100

  • duygut/hotel_booking_cancelation_with_tree_based_algorithms

    Comparing different tree-based algorithms to find the best model for cancelation prediction

    Language:Jupyter Notebook0100

  • gt0410/Unredactor

    In this project we are tryinbg to create unredactor. Unredactor will take a redacted document and the redacted flag as input, inreturn it will give the most likely candidates to fill in redacted location. In this project we are only considered about unredacting names only. The data that we are considering is imdb data set with many review files. These files are used to buils corpora for finding tfidf score. Few files are used to train and in these files names are redacted and written into redacted folder. These redacted files are used for testing and different classification models are built to predict the probabilies of each class. Top 5 classes i.e names similar to the test features are written at the end of text in unreddacted foleder.

    Language:Python0001

  • haridharanias/Ensemble-Learning

    Predicted the breast cancer in patient using Ensemble Techniques and evaluated the model

    Language:Jupyter Notebook0100

  • jdebran/KC_Practica_Machine-Learning-101

    KeepCoding Bootcamp Big Data & Machine Learning - Práctica Machine Learning 101

    Language:Jupyter Notebook0100

  • rupalshrivastava/Supervised-Machine-Learning

    Implemented support vector machines, boosting, and decision trees for classification problems. Used cross-validation for improving model accuracy. Plotted different types of learning curves like error rates vs train data size, error rates vs clock time. Compared performance using learning curves and confusion matrices across algorithms.

    Language:Jupyter Notebook0100

  • sheny2/Cloud_Detection

    classfication of cloud image pixels

    Language:R0101

  • shreyash2610/-A-Fine-Windy-Day-HackerEarth

    Problem Moving from traditional energy plans powered by fossils fuels to unlimited renewable energy subscriptions allows for instant access to clean energy without heavy investment in infrastructure like solar panels, for example. One clean energy source that has been gaining popularity around the world is wind turbines. Turbines are massive structures that are strategically placed in perpetually windy places to generate the most energy. Wind energy is generated when the power of the atmosphere’s airflow is harnessed to create electricity. Wind turbines do this by capturing the kinetic energy of the wind. Factors such as temperature, wind direction, turbine status, weather, blade length, etc. influence the amount of power generated.

    Language:Jupyter Notebook0100

  • srikanth2102/IPL_SCORE_PREDICTION

    This project focuses on predicting the IPL scores using Machine learning models with the use of Python using Scikit Learn Library. The model predicts the score after a minimum of 5 overs. The score on Testing data was 94.17%.

    Language:Jupyter Notebook0100

  • Szymon-Czuszek/Machine-Learning-Algorithms

    In this repository, I will share the materials related to machine learning algorithms, as I enrich my knowledge in this field.

    Language:Jupyter Notebook0100

  • JQmiracle/HR_Analytics

    Job Change of Data Scientists Prediction

    10

  • newking9088/building_insurance_predictive_modeling

    A comprehensive case study on implementing predictive modeling in insurance

  • svsaurav95/Zepto-analysis-

    Zepto Order Analysis to Predict Unique Orders from a range of Products

    Language:Jupyter Notebook