improving accuracy of classification
$begingroup$
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%
- Normalize numeric variables and for training data, find mean vector for Y=1 and Y=0
- for each data point, find euclidean distance from each mean vector - distance0 and distance1
- 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
asked 9 mins 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%
- Normalize numeric variables and for training data, find mean vector for Y=1 and Y=0
- for each data point, find euclidean distance from each mean vector - distance0 and distance1
- 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
asked 9 mins 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%
- Normalize numeric variables and for training data, find mean vector for Y=1 and Y=0
- for each data point, find euclidean distance from each mean vector - distance0 and distance1
- 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
asked 9 mins ago
user2543622user2543622
1236
$endgroup$
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%
- Normalize numeric variables and for training data, find mean vector for Y=1 and Y=0
- for each data point, find euclidean distance from each mean vector - distance0 and distance1
- 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
classification predictive-modeling
asked 9 mins ago
user2543622user2543622
1236
asked 9 mins ago
user2543622user2543622
1236
asked 9 mins ago
user2543622user2543622
1236
asked 9 mins ago
user2543622user2543622
1236
asked 9 mins ago
user2543622user2543622
1236
1236
add a comment |
add a comment |
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