# Lost in space solution

My solution bases on computing power and heavy ensemble learning. I use autogluon with feature selection + training on dataset subsets. For example, the first solution uses an entire train_df, the second one is a mean of solutions for even and odd rows. In general, a nth solution is a mean of solutions for subsets train_df[::n], train_df[1::n], …, train_df[n-1::n]. I apply n equal to 100. The problem seems to have high variance, so ensemble learning is very efficient. This score booster can be applied to every solution. The solution can be easily extended by adding more diversity. We can modify sample weights, a number of folds, take only a recent or oldest part of data or select different subset of features. The computing power is a main obstacle. I have provided to host a simplified solution (1 of 100) due to time limitation. Parallel computing is recommended in this case or being very patient.
[@drwtrading](https://www.kaggle.com/drwtrading)