Machine Learning Classifier use past predictions as features

Question

I want to built a binary classifier machine learning model. I want to use the model's previous predictions as features for the future predictions, to take into account that my training samples are not independent.

Is there a framework to achieve this with scikit-learn, or any other python ML library?

I know this problem could be solved with memory-based Neural Networks architectures, like RNNs, LSTMs, ..., but I would want to consider non deep learning approaches first (typically with gradient boosting models, tree based models, ...)

Answer 1

There are several statistical models which you can use for predicting the future based on the past without using deep learning; in other words, for time series forecasting.

For example, you could use ARIMA/SARIMA/SARIMAX or VAR , which are statistical models which can be used for prediction.

You can consult the following links as starting points:

1. https://machinelearningmastery.com/arima-for-time-series-forecasting-with-python/
2. https://www.machinelearningplus.com/time-series/vector-autoregression-examples-python/

Also, do not forget to delve deep into the statsmodel python library.