If I run a simple dtree regression model using data via the train_test_split function, I get nice r2 scores, and low mse values.
training_data = pandas.read_csv('data.csv',usecols=['y','x1','x2','x3'])
y = training_data.iloc[:,0]
x = training_data.iloc[:,1:]
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.33)
regressor = DecisionTreeRegressor(random_state = 0)
# fit the regressor with X and Y data
regressor.fit(X_train, y_train)
y_pred = regressor.predict(X_test)
yet if I split the data file manually into two files 2/3 train and 1/3 test. there is a column called human which gives a value 1 to 9 which human it is, i use human 1-6 for training, and 7-9 for test
i get negative r2 scores, and high mse
training_data = pandas.read_csv("train"+".csv",usecols=['y','x1','x2','x3'])
testing_data = pandas.read_csv("test"+".csv", usecols=['y','x1','x2','x3'])
y_train = training_data.iloc[:,training_data.columns.str.contains('y')]
X_train = training_data.iloc[:,training_data.columns.str.contains('|'.join(['x1','x2','x3']))]
y_test = testing_data.iloc[:,testing_data.columns.str.contains('y')]
X_test = testing_data.iloc[:,testing_data.columns.str.contains('|'.join(l_vars))]
y_train = pandas.Series(y_train['y'], index=y_train.index)
y_test = pandas.Series(y_test['y'], index=y_test.index)
regressor = DecisionTreeRegressor(random_state = 0)
regressor.fit(X_train, y_train)
y_pred = regressor.predict(X_test)
I was expecting more or less the same results, and all the data types seem the same for both calls.
What am I missing?
I'm assuming that both methods here actually do what you intend doing and the shapes of your X_train/test and y_train/tests are the same coming from both methods. You can either plot the underlying distributions of your datasets or compare your second implementation against a cross-validated model (for better rigour).
Plot the distributions (i.e. make bar charts/density plots) of the labels (y) in the initial train - test sets vs in the second ones (from the manual implementation). You can dive deeper and also plot your other columns in the data, see if anything about the distributions of your data is different between the resulting sets of the two implementations. If the distributions are different than it makes sense you get discrepancies between your two models. If your discrepancy is huge, it could be your labels (or other columns) are actually sorted for your manual implementation, so then you get very different distributions in the datasets you're comparing.
Also, if you want to make sure that your manual splitting results is a 'representative' set(that would generalise well) based on model results instead of underlying data distributions, I would compare it against the results of a cross-validated model, not one single set of results.
Essentially, although the probability is small and the train_test_split does some shuffling, you could essentially get a 'train/test' pair that is performing well just out of luck. (To reduce the chance of that without doing cross validation, I'd suggest making use of the stratify argument of the train_test_split function. then at least you're sure the first implementation 'tries harder' to get balanced train/test pairs.)
If you decide to cross validate (with test_train_split), you get an average model prediction for the folds and a confidence intervals around it and can check if your second model results fall within that interval. If it doesn't again, it just means your split is actually 'corrupted' somehow (e.g. by having sorted values).
P.S. I'd also add that Decision Trees are models that are known to overfit massively [1]. Maybe use a random forest instead? (you should get more stable results due to bootstraping/bagging which would act similarly to cross-validating to reduce the chance of overfitting.)
1 - http://cv.znu.ac.ir/afsharchim/AI/lectures/Decision%20Trees%203.pdf