Linked Questions
12 questions linked to/from In supervised learning, why is it bad to have correlated features?
27
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Why do we need to discard one dummy variable?
I have learned that, for creating a regression model, we have to take care of categorical variables by converting them into dummy variables. As an example, if, in our data set, there is a variable ...
10
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3
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Can we remove features that have zero-correlation with the target/label?
So I draw a pairplot/heatmap from the feature correlations of a dataset and see a set of features that bears Zero-correlations both with:
every other feature and
also with the target/label
....
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6
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1
answer
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How high of a correlation coefficient of a feature with a target variable is considered too high?
Currently my classification model is doing too well on all of the train, validation, and test datasets. I'm assuming there is a data leakage in the features, and therefore I've computed the ...
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2
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3
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Can GLM( generalized linear method) handle the collinearity between the predictor variables in a regression-analysis?
I'm a beginner in Machine learning and I've studied that collinearity among the predictor variables of a model is a huge problem since it can lead to unpredictable model behaviour and a large error. ...
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1
vote
1
answer
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decision -tree regression to avoid multicollinearity for regression model?
I read in comments a recommendation for decision tree´s instead of linear models like neural network, when the dataset has many correlated features. Because to avoid multicollinearity.
A similar ...
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4
votes
3
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Evaluating new features
How should I evaluate whether new features are effective or not? Should I build a new model with the new features then compare with the old one with the same hyper parameter?
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2
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2
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Removing features that correlate with the target label
I know that it is better to remove correlated features, but what if the feature correlates with the target label? So, it gives the most information for prediction? Do I need to remove features that ...
- 131
2
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2
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How to select features for a ML model
I have a dataset with 5K records for binary classification problem.
My features are min_blood_pressure, max_blood_pressure, <...
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0
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2
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If I have two variables with strong correlation, should I delete one and leave the other in my data
I have a large dataset, where I should make a binary prediction. The fact is that, after analyzing the data, I found that some variables are positively correlated to each other. So, I was wondering ...
0
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1
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Should highly correlated features be removed, even if they have different type of information?
A quick example for this: we have many feature and two of them are policy count and premium_total (for all policies). We are predicting the expected claim amount with GBM or RF. Both policy_count and ...
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1
vote
0
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Feature creation: Problem with correlated features?
I recently started to read about feature creation. I've seen some general guidelines although I am not really sure if they are completely true, for example:
1 - Linear classifiers for binary ...
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0
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0
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Which redundant feature should be use
I have two redundant features. A & B with 0.85 correlation. I know only one of them should be used to trained my model, but which feature should i use? A or B? Is there any method that can i use ...
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