I am using Abalon data for classification from UCI(https://archive.ics.uci.edu/ml/machine-learning-databases/abalone/abalone.data). I have scaled data and used TSNE for visualization.

x=data.drop('15', axis=1)
import matplotlib as plt
from sklearn.preprocessing import StandardScaler
from sklearn.manifold import Isomap,TSNE

Visualization of data in 2D

from sklearn.ensemble import GradientBoostingClassifier
from sklearn.cross_validation import cross_val_score,train_test_split
from sklearn.metrics import classification_report,f1_score

print classification_report(y_true=y_test,y_pred=gb.predict(x_test))

This model is failing poorly as from the classification report its showing all metrics recall, f1, precision as .23,.22,.24.

I understand its multiclass classification with high class imbalance. What can I do to improve the model?


Although tuning hyper parameters is always worthwhile, with scores that low, I think you should consider other algorithms. I don't understand the nature of the data, but it could be very heterogeneous (high variance), making it near impossible for some algorithms. I don't see much feature engineering you can do for this specific data, even though that is always a good place to start when trying to get better performance. But ultimately, I can only advise you try some other algos before attempting tuning. Good luck!

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Gradient Boosting is a good approach to tackle multiclass problem that suffers from class imbalance issue. In your cross validation you're not tuning any hyper-parameters for GB. I would recommend following this link and try tuning few parameters.


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