I have a well defined data where i have cleaned up my data to final form which has 20 features mapping to a number between 1 to 100. Upto 5 features are enabled(value set to 1) for each row. The data looks something like below
Result|f1|f2|...f19|f20 45 |0 | 1|... 1 | 0 92 |0 | 0|... 1 | 1
I'm trying to build machine learning models that can give me good accuracy and preferably models which can handle
warm_start since each iteration generates 1 row that i need to fit into existing build model.
below are 2 classifiers that i tried to set some baseline
randclf = RandomForestClassifier(n_estimators=50) decclf = DecisionTreeClassifier(criterion = "gini", random_state = 100,max_depth=3, min_samples_leaf=5)
However even with 100,000 records i'm getting very poor result with accuracy around 15-20%. considering how predictable data is(data is generated based on finite set of rules) i was expecting very high accuracy.
I'm i doing something wrong, i want get the high accuracy in classifying data(predicting Result) based on features given, can you suggest some models that might work well this kind of data. what about tensorflow and neural network approach?
Update: Data imbalance is something i cant help as they are generated based on rules.