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improving accuracy of classification

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I have data with 95 numeric variables and 5 categorical variables. My Y has 2 values. I built a decision tree and my accuracy was 81.8%. Then I created 3 new variables as follows. It improved accuracy to 84.3%

1. Normalize numeric variables and for training data, find mean vector for Y=1 and Y=0


2. for each data point, find euclidean distance from each mean vector - distance0 and distance1


3. third variable will be 0 if distance0 is <= distance1

I was wondering if there is any other new variables that i could create to improve the accuracy

I used a decision tree as it is fast to build and gives me indication whether a newly created variable is useful or not.

Please let me know if you have any thoughts

classification predictive-modeling

share|improve this question

asked 6 hours ago

user2543622user2543622

1236

$endgroup$

add a comment | 

0

$begingroup$

I have data with 95 numeric variables and 5 categorical variables. My Y has 2 values. I built a decision tree and my accuracy was 81.8%. Then I created 3 new variables as follows. It improved accuracy to 84.3%

1. Normalize numeric variables and for training data, find mean vector for Y=1 and Y=0


2. for each data point, find euclidean distance from each mean vector - distance0 and distance1


3. third variable will be 0 if distance0 is <= distance1

I was wondering if there is any other new variables that i could create to improve the accuracy

I used a decision tree as it is fast to build and gives me indication whether a newly created variable is useful or not.

Please let me know if you have any thoughts

classification predictive-modeling

share|improve this question

asked 6 hours ago

user2543622user2543622

1236

$endgroup$

add a comment | 

$begingroup$

I have data with 95 numeric variables and 5 categorical variables. My Y has 2 values. I built a decision tree and my accuracy was 81.8%. Then I created 3 new variables as follows. It improved accuracy to 84.3%

1. Normalize numeric variables and for training data, find mean vector for Y=1 and Y=0


2. for each data point, find euclidean distance from each mean vector - distance0 and distance1


3. third variable will be 0 if distance0 is <= distance1

I was wondering if there is any other new variables that i could create to improve the accuracy

I used a decision tree as it is fast to build and gives me indication whether a newly created variable is useful or not.

Please let me know if you have any thoughts

classification predictive-modeling

share|improve this question

asked 6 hours ago

user2543622user2543622

1236

$endgroup$

share|improve this question

asked 6 hours ago

user2543622user2543622

1236

share|improve this question

asked 6 hours ago

user2543622user2543622

1236

asked 6 hours ago

user2543622user2543622

1236

asked 6 hours ago

user2543622user2543622

1236