When should we discretize/bin independent variables/features and when should not?

My attempts to answer the question:

• In general, we should not bin, because binning will lose information.
• Binning is actually increasing the degree of freedom of the model, so, it is possible to cause over-fitting after binning. If we have a "high bias" model, binning may not be bad, but if we have a "high variance" model, we should avoid binning.
• It depends on what model we are using. If it is a linear mode, and data has a lot of "outliers" binning probability is better. If we have a tree model, then, outlier and binning will make too much difference.

Am I right? and what else?

I thought this question should be asked many times but I cannot find it in CV only these posts

Should we bin continuous variables?

What is the benefit of breaking up a continuous predictor variable?

When should we discretize/bin independent variables/features and when should not?

My attempts to answer the question:

• In general, we should not bin, because binning will lose information.
• Binning is actually increasing the degree of freedom of the model, so, it is possible to cause over-fitting after binning. If we have a "high bias" model, binning may not be bad, but if we have a "high variance" model, we should avoid binning.
• It depends on what model we are using. If it is a linear mode, and data has a lot of "outliers" binning probability is better. If we have a tree model, then, outlier and binning will make too much difference.

Am I right? and what else?

I thought this question should be asked many times but I cannot find it in CV only these posts

Should we bin continuous variables?

What is the benefit of breaking up a continuous predictor variable?

When should we discretize/binning independent variables  / features and when not?

My attempts to answer the question:

• In general, we should not bin, because binning will lose information.
• Binning is actually increasing the degree of freedom of the model, so, it is possible to cause over-fitting after binning. If we have a "high bias" model, binning may not be bad, but if we have a "high variance" model, we should avoid binning.
• It depends on what model we are using. If it is a linear mode, and data has a lot of "outliers" binning probability is better. If we have a tree model, then, outlier and binning will make too much difference.

Am I right? and what else?

I thought this question should be asked many times but I cannot find it in CV only these posts

Should we bin continuous variables?

What is the benefit of breaking up a continuous predictor variable?

When should we discretize/bin independent variables/features and when should not?

My attempts to answer the question:

• In general, we should not bin, because binning will lose information.
• Binning is actually increasing the degree of freedom of the model, so, it is possible to cause over-fitting after binning. If we have a "high bias" model, binning may not be bad, but if we have a "high variance" model, we should avoid binning.
• It depends on what model we are using. If it is a linear mode, and data has a lot of "outliers" binning probability is better. If we have a tree model, then, outlier and binning will make too much difference.

Am I right? and what else?

I thought this question should be asked many times but I cannot find it in CV only these posts

Should we bin continuous variables?

What is the benefit of breaking up a continuous predictor variable?

When should we discretize/binning independent variables / features and when not?

My attempts to answer the question:

• In general, we should not bin, because binning will lose information.
• Binning is actually increasing the degree of freedom of the model, so, it is possible to cause over-fitting after binning. If we have a "high bias" model, binning may not be bad, but if we have a "high variance" model, we should avoid binning.
• It depends on what model we are using. If it is a linear mode, and data has a lot of "outliers" binning probability is better. If we have a tree model, then, outlier and binning will make too much difference.

Am I right? and what else?

• I thought this question should be asked many times but I cannot find it in CV only these posts

Should we bin continuous variables?

What is the benefit of breaking up a continuous predictor variable?

• Thanks for the great answer by Alexis. In addition, Is there any publications on "machine learning" that empirically compare bin vs not bin on many real world data set?

When should we discretize/binning independent variables / features and when not?

My attempts to answer the question:

• In general, we should not bin, because binning will lose information.
• Binning is actually increasing the degree of freedom of the model, so, it is possible to cause over-fitting after binning. If we have a "high bias" model, binning may not be bad, but if we have a "high variance" model, we should avoid binning.
• It depends on what model we are using. If it is a linear mode, and data has a lot of "outliers" binning probability is better. If we have a tree model, then, outlier and binning will make too much difference.

Am I right? and what else?

I thought this question should be asked many times but I cannot find it in CV only these posts

Should we bin continuous variables?

What is the benefit of breaking up a continuous predictor variable?