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what make lightGBM run faster than XGBoost?

xgboost

asked 8 mins ago Victor Luu 91

3

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2

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379

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Inputting (a lot of )data into a dataframe one row at a time

python pandas numpy dataframe data-wrangling

modified 15 secs ago Lorenz 101

2

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1

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61

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Transformer-based architectures for regression tasks

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modified 44 mins ago Community♦ 1

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11

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Is always converting a input vector into matrix and apply cnn's good idea?

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modified 59 mins ago satinder singh 1

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11

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What is the best way to train a gradient boosted model on a binomial dataset where the number of “observations” for each instance varies?

python machine-learning regression classification xgboost

modified 1 hour ago lavendarlemur 1

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1

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14

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How can i tell if my model is overfitting from the distribution of predicted probabilities?

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answered 1 hour ago Ben Reiniger 5,295

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10

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How can we perfrom pre fitted standard scalar inverse transform on y variable in pipeline

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modified 1 hour ago Mangesh Divate 13

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0

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3

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Machine Learning Recommender high data intensity

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asked 1 hour ago Allen Wu 13

1

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1

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11

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How do I get one overall prediction, where each data point has many pictures?

machine-learning neural-network deep-learning classification cnn

modified 1 hour ago Community♦ 1

1

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1

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17

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How to avoid confusion between Percentage and Percentage Points?

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answered 2 hours ago Sammy 3,272

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1

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432

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While retraining a pretrained model, getting: ValueError: Input 0 is incompatible with layer flatten_1: expected min_ndim=3, found ndim=2

neural-network deep-learning keras

modified 2 hours ago Community♦ 1

1

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1

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54

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Naive Bayes always predicting the same label

machine-learning python naive-bayes-classifier

modified 3 hours ago Community♦ 1

0

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1

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57

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Pandas/Python - comparing two columns for matches not in the same row

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modified 3 hours ago Steven 51

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8

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Fine tuning a automatic speech recognition model with my own dataset

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modified 4 hours ago shepan6 844

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what make lightGBM run faster than XGBoost?

Inputting (a lot of )data into a dataframe one row at a time

Transformer-based architectures for regression tasks

Is always converting a input vector into matrix and apply cnn's good idea?

What is the best way to train a gradient boosted model on a binomial dataset where the number of “observations” for each instance varies?

How can i tell if my model is overfitting from the distribution of predicted probabilities?

How can we perfrom pre fitted standard scalar inverse transform on y variable in pipeline

Machine Learning Recommender high data intensity

How do I get one overall prediction, where each data point has many pictures?

How to avoid confusion between Percentage and Percentage Points?

While retraining a pretrained model, getting: ValueError: Input 0 is incompatible with layer flatten_1: expected min_ndim=3, found ndim=2

Naive Bayes always predicting the same label

Pandas/Python - comparing two columns for matches not in the same row

Fine tuning a automatic speech recognition model with my own dataset

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