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How valuable is a categorical feature that has a predominant category over all other ones?
Is automatic feature detection feasible?How to train neural network that has different kind of layersHow to find splits in data so that each split has equal weighting according to function fHow can I make a prediction in a regression model if a category has not been observed already?How to continue incremental learning when a categorical variable has been assigned additional category labels?How to implement feature selection for categorical variables (especially with many categories)?ML Models: How to handle categorical feature with over 1000 unique valuesClassification: how to handle reviews/long english words in feature set with all other numerical featureshow to deal with two high correlations feature which both has a low correlation with targetHow to do feature engineering to the stripplot where the target, `tradeMoney`, has obviously lower than 5000 when 'rentType' is 'shared_rent'?
.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty margin-bottom:0;
$begingroup$
Is a categorical feature that has almost equally distributed in it's category more important or the one which one of it's category is predominant over all other ones?
In data prepossessing step for "House Price" competition, I want to decide if Street feature is important or I can drop it from data set in order to avoid over-fitting. So I have plotted a swarm-plot as follows:
How should I interpret this plot? Does it show that the Street plot can be dropped, or it says it is valuable for creating a model?
machine-learning data-mining feature-selection data-cleaning feature-engineering
asked 8 hours ago
Ali Majed HAAli Majed HA
1236
$endgroup$
add a comment |
$begingroup$
Is a categorical feature that has almost equally distributed in it's category more important or the one which one of it's category is predominant over all other ones?
In data prepossessing step for "House Price" competition, I want to decide if Street feature is important or I can drop it from data set in order to avoid over-fitting. So I have plotted a swarm-plot as follows:
How should I interpret this plot? Does it show that the Street plot can be dropped, or it says it is valuable for creating a model?
machine-learning data-mining feature-selection data-cleaning feature-engineering
asked 8 hours ago
Ali Majed HAAli Majed HA
1236
$endgroup$
add a comment |
$begingroup$
Is a categorical feature that has almost equally distributed in it's category more important or the one which one of it's category is predominant over all other ones?
In data prepossessing step for "House Price" competition, I want to decide if Street feature is important or I can drop it from data set in order to avoid over-fitting. So I have plotted a swarm-plot as follows:
How should I interpret this plot? Does it show that the Street plot can be dropped, or it says it is valuable for creating a model?
machine-learning data-mining feature-selection data-cleaning feature-engineering
asked 8 hours ago
Ali Majed HAAli Majed HA
1236
$endgroup$
Is a categorical feature that has almost equally distributed in it's category more important or the one which one of it's category is predominant over all other ones?
In data prepossessing step for "House Price" competition, I want to decide if Street feature is important or I can drop it from data set in order to avoid over-fitting. So I have plotted a swarm-plot as follows:
How should I interpret this plot? Does it show that the Street plot can be dropped, or it says it is valuable for creating a model?
machine-learning data-mining feature-selection data-cleaning feature-engineering
machine-learning data-mining feature-selection data-cleaning feature-engineering
asked 8 hours ago
Ali Majed HAAli Majed HA
1236
asked 8 hours ago
Ali Majed HAAli Majed HA
1236
asked 8 hours ago
Ali Majed HAAli Majed HA
1236
asked 8 hours ago
Ali Majed HAAli Majed HA
1236
asked 8 hours ago
Ali Majed HAAli Majed HA
1236
1236
add a comment |
add a comment |
1 Answer
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$begingroup$
Feature importance is an empirical question. Train a model with the feature in it. Train another model without the feature in it. Then see which model does better when predicting new data, aka the test dataset. The difference between model performance on train and test datasets is one way to measure overfitting.
answered 8 hours ago
Brian SpieringBrian Spiering
4,6861130
$endgroup$
add a comment |
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1 Answer
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1 Answer
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active
oldest
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$begingroup$
Feature importance is an empirical question. Train a model with the feature in it. Train another model without the feature in it. Then see which model does better when predicting new data, aka the test dataset. The difference between model performance on train and test datasets is one way to measure overfitting.
answered 8 hours ago
Brian SpieringBrian Spiering
4,6861130
$endgroup$
add a comment |
$begingroup$
Feature importance is an empirical question. Train a model with the feature in it. Train another model without the feature in it. Then see which model does better when predicting new data, aka the test dataset. The difference between model performance on train and test datasets is one way to measure overfitting.
answered 8 hours ago
Brian SpieringBrian Spiering
4,6861130
$endgroup$
add a comment |
$begingroup$
Feature importance is an empirical question. Train a model with the feature in it. Train another model without the feature in it. Then see which model does better when predicting new data, aka the test dataset. The difference between model performance on train and test datasets is one way to measure overfitting.
answered 8 hours ago
Brian SpieringBrian Spiering
4,6861130
$endgroup$
Feature importance is an empirical question. Train a model with the feature in it. Train another model without the feature in it. Then see which model does better when predicting new data, aka the test dataset. The difference between model performance on train and test datasets is one way to measure overfitting.
answered 8 hours ago
Brian SpieringBrian Spiering
4,6861130
answered 8 hours ago
Brian SpieringBrian Spiering
4,6861130
answered 8 hours ago
Brian SpieringBrian Spiering
4,6861130
answered 8 hours ago
Brian SpieringBrian Spiering
4,6861130
4,6861130
add a comment |
add a comment |
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