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Toros91
Toros91
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I'm new to machine learning domain. I'm goingI want to build a Classification tree for binaryBinary classification of data. 

I have a training data set of 269 records out of which 56 records belong to 'yes' class and 213 records to 'no' class. 

Is this data imbalanced for building CART model? Do I need to undersample 'no' class records? Also from Gini index, Chi-squareSquare and information gainInformation Gain, which algorithm is best for node splitting?

P.S.:- I can't increase the size of the dataset further.

I'm new to machine learning domain. I'm going to build a Classification tree for binary classification of data. I have training data set of 269 records out of which 56 records belong to 'yes' class and 213 records to 'no' class. Is this data imbalanced for building CART model? Do I need to undersample 'no' class records? Also from Gini index, Chi-square and information gain, which algorithm is best for node splitting?

P.S.:- I can't increase the size of the dataset further.

I'm new to machine learning domain. I want to build a Classification tree for Binary classification of data. 

I have a training data set of 269 records out of which 56 records belong to 'yes' class and 213 records to 'no' class. 

Is this data imbalanced for building CART model? Do I need to undersample 'no' class records? Also from Gini index, Chi-Square and Information Gain, which algorithm is best for node splitting?

P.S.:- I can't increase the size of the dataset further.

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Scorpionk
Scorpionk
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Identifying whether training dataset is imbalanced in CART

I'm new to machine learning domain. I'm going to build a Classification tree for binary classification of data. I have training data set of 269 records out of which 56 records belong to 'yes' class and 213 records to 'no' class. Is this data imbalanced for building CART model? Do I need to undersample 'no' class records? Also from Gini index, Chi-square and information gain, which algorithm is best for node splitting?

P.S.:- I can't increase the size of the dataset further.