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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.

boosting-tree wandb weights-and-biases

Updated Jan 30, 2024
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unnatibshah / LASSO-and-Boosting-for-Regression

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LASSO and Boosting for Regression on Communities and Crime data

xgboost ridge-regression pcr linear-models multivariate-regression lasso-regression correlation-matrix data-imputation boosting-tree coefficient-of-variation plots-in-python

Updated Nov 6, 2023
Jupyter Notebook

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

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

machine-learning deep-learning tensorflow keras xgboost gradient-boosting-classifier imageclef boosting-tree lifeclef clef xgboost-classifier boosting-ensemble

Updated Mar 3, 2023
Python

sheny2 / Cloud_Detection

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classfication of cloud image pixels

machine-learning-algorithms supervised-learning logistic-regression lda-model classfication boosting-tree

Updated Dec 7, 2022
R

JQmiracle / HR_Analytics

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Job Change of Data Scientists Prediction

machine-learning random-forest linear-regression data-analysis logistic-regression decision-tree-classifier boosting-tree

Updated Jun 13, 2022

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.…

machine-learning random-forest text-classification python3 feature-extraction corpora text-analytics classification-model boosting-tree tfidf-scores

Updated Nov 10, 2021
Python

prs98 / Telecom_Churn_Analysis

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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.

data-science machine-learning random-forest decision-trees telecommunications cohort-analysis boosting-tree

Updated Sep 28, 2021
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srikanth2102 / IPL_SCORE_PREDICTION

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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%.

machine-learning deep-learning random-forest linear-regression scikit-learn artificial-intelligence pyhton decision-tree ipl mlp-regressor boosting-tree

Updated Jul 26, 2021
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shreyash2610 / -A-Fine-Windy-Day-HackerEarth

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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 str…

machine-learning regression data-visualization hackerearth beginner catboost ensemble-machine-learning boosting-tree voting-regressor fine-windy-day

Updated Jul 20, 2021
Jupyter Notebook

adelekuzmiakova / CS229-machine-learning-solar-energy-predictions

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

python machine-learning deep-learning tensorflow matlab pca-analysis data-processing lstm-neural-networks solar-energy time-series-prediction time-series-forecasting boosting-tree

Updated Jun 29, 2021
Python

haridharanias / Ensemble-Learning

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

boosting-algorithms ensemble-model ensemble-classifier ensemble-machine-learning boosting-tree

Updated Apr 22, 2021
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xiaodaigh / JLBoostMLJ.jl

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MLJ.jl interface for JLBoost.jl

machine-learning ml boosting-algorithms boosting-tree boosting-machine mlj

Updated Mar 29, 2021
Julia

rupalshrivastava / Supervised-Machine-Learning

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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.

classification decision-trees boosting-algorithms support-vector-machines decision-tree-classifier boosting boosting-tree