About the dataset I have a training dataset of
- 129 columns(last column being the classes, i.e., y values)
- 6068 rows
I have to train some algo to do binary classification. The data set has
- 701 examples of type A
- 5367 examples of type B
The test set consists of 1398 examples. Here is the accuracy I got for various algorithms.
- voting ensemble -> 0.73963
- stochastic gradient boosting -> 0.77682
- Adaboost -> 0.75107
- bagging classifier(Decision tree) -> 0.76538
- Random Forests -> 0.75250
- Extra trees -> 0.75393
All the above results are from kaggle so they probably are just from half the test set. The above methods were implemented using scikit-learn library in python
Could someone please suggest ways to improve accuracy, may be by things like dimensionality reduction or better algorithms. Also, please provide sample code in possible.