I'm currently training a random forest on some data I have and I'm finding that the model performs better on the validation set, and even better on the test set, than on the train set. Here are some details of what I'm doing - please let me know if I've missed any important information and I will add it in.
My question
Am I doing anything obviously wrong and do you have any advice for how I should improve my approach because I just can't believe that I'm doing it right when my model predicts significantly better on unseen data than training data!
Data
My underlying data consists of tables of features describing customer behaviour and a binary target (so this is a binary classification problem). Technically I have one such table per month and I tend to use several months of data to train and then a different month to predict (e.g. Train on Apr, May and Predict on Jun)
Generally this means I end up with a training dataset of about 100k rows and 20 features (I've previously looked into feature selection and found a set of 7 features which seem to perform best, so have been using these lately). My prediction set generally has around 50k rows.
My dataset is heavily unbalanced (approximately 2% incidence of target feature), so I'm using oversampling techniques - more on that below.
Method
I've searched around online quite a lot and this has led me to the following approach:
- Take scaleable (continuous) features in the training data and standardise them (currently using sklearn StandardScaler)
- Take categorical features and encode them into separate binary columns (one-hot) using Pandas get_dummies function
- Remove 10% of the training data to form a validation set (I'm currently using a random seed in this process for comparability whilst I vary different things such as hyperparameters in the model)
- Take the remaining 90% of training data and perform a grid search across a few parameters of the RandomForestClassifier() (currently min_samples_split, max_depth, n_estimators and max_features)
- Within each hyperparameter combination from the grid I perform kfold validation with 5 folds and using a random state
- Within each fold I oversample my minority class for training data only (sometimes using imbalanced-learn's RandomOverSampler() and sometimes using SMOTE() from the same package), train the model on the training data and then apply the model to the kth fold and record performance metrics (precision, recall, F1 and AUC)
- Once I've been through 5 folds on each hyperparameter combination I find the best F1 score (and best precision if two combinations are tied on F1 score) and retrain a random forest on the entire 90% training data using those hyperparameters. During this step I use the same oversampling technique as I did in the kfold process
- I then use this model to make predictions on the 10% of training data that I put aside earlier as a validation set, evaluating the same metrics as above
- Finally I have a test set, which is actually based on data from another month, which I apply the already trained model to and evaluate the same metrics
Outcome
At the moment I'm finding that my training set achieves an F1 score of around 30%, the validation set is consistently slightly higher than this at around 36% (mostly driven by a much better precision than the training data e.g. 60% vs. 30%) and then the testing set is getting an F1 score of between 45% and 50% which is again driven by a better precision (around 65%)
Notes
Please do ask about any details I haven't mentioned; I've had my stuck in this for weeks and so have doubtless omitted some details
- I've had a brief look (not a systematic analysis) of the stability of metrics between folds in the kfold validation and it seems that they aren't varying very much, so I'm fairly happy with the stability of the model here
- I'm actually performing the grid search manually rather than using a Python pipeline because try as I might I couldn't get imbalanced-learn's Pipeline function to work with the oversampling functions and so I run a loop with combinations of hyperparameters, but I'm confident that this isn't impacting the results I've talked about above in an adverse way
- When I apply the final model to the prediction data (and get an F1 score around 45%) I also apply it back to the training data itself out of interest and get F1 scores around 90% - 100%. I suppose this is to be expected as the model is trained and predicts on almost exactly the same data (except the 10% holdout validation set)
