# Both train and test scores high. Could my model be overfitting?

all. Currently I am working in a regressor to predict the median house value using 1990 census as a dataset (the same on Aurélien Géron used on his book).

My best result so far has been a score of ~0.96 for the training set and ~0.80 and for the test set with a bagging regressor. Here's how I had been doing:

precisao_treino = clf.score(dados_treino_feat,dados_treino_targ) #training score

(0.9698880465469455, 0.8087685894971024)


But here is where I get confused. That very high training score indicate that there is a potential overfit on my model. However, the test score is maintained a relative good score. So I don't know if my model is overfitting or if doing well at predicting. Could someone explain to me whether this a bad or good model?

Here's how my final dataset looks like, with one-hot-encoding:

['longitude',
'latitude',
'housing_median_age',
'total_rooms',
'total_bedrooms',
'population',
'households',
'median_income',
'median_house_value',
'<1H OCEAN',
'INLAND',
'ISLAND',
'NEAR BAY',
'NEAR OCEAN']


My solution to removing outliers:

df = df[(np.abs(zscore(df)) < 6).all(axis=1)]


My model:

clf = ensemble.BaggingRegressor(n_estimators=20)