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improving accuracy of classification
Improving Naive Bayes accuracy for text classificationOver-fitting issue in a classification problem (unbalanced data)Aggregating Decision TreesDecision Tree generating leaves for only one caseNeed Advice, Classification Problem in Python: Should I use Decision tree, Random Forests, or Logistic Regression?Fetching rules from rpart using caret packageEstimating Propensity Score via Regression Trees (in R Using rpart)Desicision tree classification with a “false” attributeProblems successfuly implementing stacked autoencoder in binary classification problemWhy is recall so high?
$begingroup$
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 32 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 32 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 32 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 32 mins ago
user2543622user2543622
1236
asked 32 mins ago
user2543622user2543622
1236
asked 32 mins ago
user2543622user2543622
1236
asked 32 mins ago
user2543622user2543622
1236
asked 32 mins ago
user2543622user2543622
1236
1236
add a comment |
add a comment |
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